US5892853A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Methods, apparatus and program storage device for removing scratch or wire noise, and recording media therefor
Assignee: Sony Corp
•
Filing: 1997-07-23
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Granted: 1999-04-06
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Expires: 2017-07-23
Abstract
Abstract Methods and apparatus for removing scratch and wire noise from digitized images handle the difficult case of non-uniformly shaded images which is the common situation in real applications. The methods and apparatus also utilize a soft edge noise mask to allow a smoother blending of the reconstructed noise and the image around the noise. wire noise removal from digitized images wherein a scratched portion of a digitized image which requires repair or noise removal is identified and a binary mask is generated which distinguishes the defined scratched portion from the other portions of the digitized image. The methods and apparatus utilize both the spatial and frequency domains to effectively reconstruct many contiguous noisy pixels, reconstruct textures even when they are large featured, maintain sharpness and maintain continuity of features across a noisy region. A set of instructions for executing the methods is contained in a program storage device. Also, a recording media contains digital data generated using the methods.
Inventors
- 👤 Anil Hirani
- 👤 Takashi Totsuka
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 13:41
CPC Classifications
H; H04; H04N; H04N1/00; H04N1/40; H04N1/409; H04N1/4097; G; G06; G06T; G06T5/00; G06T5/20; H04N1/38
Claims Analysis
Total Claims: 108
Claim 1
Independent Claim
173 words
A method of removing noise from image data, comprising the steps of: defining an area of noise on the image data; generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining repair image data including the defined area of noise; defining sample image data which is similar to the repair image data; creating from the noise mask a soft noise mask having a soft edge; performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; and generating new image data using the new repair image data, the repair image data and the soft noise mask.
Claim 2
Independent Claim
173 words
A method of removing noise from image data, comprising the steps of: defining an area of noise on the image data; generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining repair image data including the defined area of noise; defining sample image data which is similar to the repair image data; creating from the noise mask a soft noise mask having a soft edge; performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; and generating new image data using the new repair image data, the repair image data and the soft noise mask.
Claim 3
Dependent Claim
24 words
The method of claim 1, further comprising the step of: creating mask repair image data in accordance with data of the soft noise mask.
Claim 4
Dependent Claim
107 words
The method of claim 1, wherein the step of performing a frequency transform to the sample image data calculates a fast fourier transform of the sample image data to generate fast fourier transform data of the sample image data; the step of performing a frequency transform to the repair image data calculates a fast fourier transform of the repair image data to generate fast fourier transform data of the repair image data; the step of performing an inverse transform calculates an inverse fast fourier transform using the fast fourier transform data of the sample image data and the fast fourier transform data of the repair image data.
Claim 5
Dependent Claim
101 words
The method of claim 3, wherein the step of calculating a fast fourier transform of the sample image data generates at least magnitudes; the step of calculating a fast fourier transform of the repair image data generates a phase and magnitudes; and the step of calculating an inverse fast fourier transform generates the new repair image data using a phase of the fast fourier transform data of the repair image data and one of the magnitudes of the fast fourier transform data of the sample image data and the magnitudes of the fast fourier transform data of the repair image data.
Claim 6
Dependent Claim
75 words
The method of claim 4, further comprising the step of: comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the step of performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 7
Dependent Claim
28 words
The method of claim 5, wherein a magnitude of a DC component is selected as a magnitude of the fast fourier transform data of the repair image data.
Claim 8
Dependent Claim
19 words
The method of claim 1, further comprising the step of: conforming the repair image data to a predetermined value.
Claim 9
Dependent Claim
33 words
The method of claim 1, further comprising the steps of: receiving the image data; displaying the image data; and storing the soft noise mask, the repair image data and the sample image data.
Claim 10
Dependent Claim
34 words
The method of claim 1, wherein the noise mask is binary data such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 11
Dependent Claim
56 words
The method of claim 1, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 12
Dependent Claim
75 words
The method of claim 10, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 13
Dependent Claim
49 words
The method of claim 1, wherein the steps of performing a frequency transform to the sample image data, performing the frequency transform to the repair image data, performing an inverse transform of the frequency transformed data and generating new image data are recursively performed to generate new image data.
Claim 14
Independent Claim
262 words
A method of removing noise from image data, comprising the steps of: defining an area of noise on the image data; generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining sample image data which is similar to the repair image data; filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; and generating new image data using the new high pass filtered repair image data and the low pass filtered image data.
Claim 15
Independent Claim
262 words
A method of removing noise from image data, comprising the steps of: defining an area of noise on the image data; generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining sample image data which is similar to the repair image data; filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; and generating new image data using the new high pass filtered repair image data and the low pass filtered image data.
Claim 16
Dependent Claim
52 words
The method of claim 13, further comprising the step of: creating from the noise mask a soft noise mask having a soft edge, wherein the step of generating new high pass filtered image data uses the new high pass filtered repair image data, the repair image data and the soft noise mask.
Claim 17
Dependent Claim
26 words
The method of claim 13, further comprising the step of: creating mask repair image data in accordance with the noise mask and the repair image data.
Claim 18
Dependent Claim
27 words
The method of claim 14, further comprising the step of: creating mask repair image data in accordance with the soft noise mask and the repair image data.
Claim 19
Dependent Claim
135 words
The method of claim 13, wherein the step of performing a frequency transform to the high pass filtered sample image data calculates a fast fourier transform of the high pass filtered sample image data to generate fast fourier transform data of the high pass filtered sample image data; the step of performing the frequency transform to the high pass filtered repair image data calculates a fast fourier transform of the high pass filtered repair image data to generate fast fourier transform data of the high pass filtered repair image data; and the step of performing an inverse transform of the frequency transform calculates an inverse fast fourier transform using the fast fourier transform data of the high pass filtered sample image data and the fast fourier transform data of the high pass repair image data.
Claim 20
Dependent Claim
131 words
The method of claim 17, wherein the step of performing a frequency transform to the high pass filtered sample image data generates at least magnitudes of the fast fourier transform thereof; the step of performing the frequency transform to the high pass filtered repair image data generates a phase and magnitudes of the fast fourier transform thereof; and the step of performing an inverse transform of the frequency transform generates the new high pass filtered repair image data using a phase of the fast fourier transform data of the high pass filtered repair image data and one of the magnitudes of the fast fourier transform data of the high pass filtered sample image data and the magnitudes of the fast fourier transform data of the high pass filtered repair image data.
Claim 21
Dependent Claim
75 words
The method of claim 18, further comprising the step of: comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the step of performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 22
Dependent Claim
28 words
The method of claim 19, wherein a magnitude of a DC component is selected as the magnitude of the fast fourier transform data of the repair image data.
Claim 23
Dependent Claim
23 words
The method of claim 13, further comprising the step of: conforming the new high pass filtered repair image data to a predetermined value.
Claim 24
Dependent Claim
36 words
The method of claim 13, wherein the noise mask is binary data such that such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 25
Dependent Claim
56 words
The method of claim 14, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 26
Dependent Claim
75 words
The method of claim 23, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 27
Dependent Claim
107 words
The method of claim 13, wherein the steps of filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data, performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data, and performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data are performed recursively to generate the new image data.
Claim 28
Independent Claim
188 words
An apparatus for removing noise from image data, comprising: means for defining an area of noise on the image data; means for generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; means for defining repair image data including the defined area of noise; means for defining sample image data which is similar to the repair image data; means for creating from the noise mask a soft noise mask having a soft edge; means for performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; means for performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; means for performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; and means for generating new image data using the new repair image data, the repair image data and the soft noise mask.
Claim 29
Independent Claim
188 words
An apparatus for removing noise from image data, comprising: means for defining an area of noise on the image data; means for generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; means for defining repair image data including the defined area of noise; means for defining sample image data which is similar to the repair image data; means for creating from the noise mask a soft noise mask having a soft edge; means for performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; means for performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; means for performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; and means for generating new image data using the new repair image data, the repair image data and the soft noise mask.
Claim 30
Dependent Claim
23 words
The apparatus of claim 26, further comprising: means for creating mask repair image data in accordance with data of the soft noise mask.
Claim 31
Dependent Claim
108 words
The apparatus of claim 26, wherein the means for performing a frequency transform to the sample image data calculates a fast fourier transform of the sample image data to generate fast fourier transform data of the sample image data; the means for performing a frequency transform to the repair image data calculates a fast fourier transform of the repair image data to generate fast fourier transform data of the repair image data; and the means for performing an inverse transform calculates an inverse fast fourier transform using the fast fourier transform data of the sample image data and the fast fourier transform data of the repair image data.
Claim 32
Dependent Claim
101 words
The apparatus of claim 28, wherein the means for calculating a fast fourier transform of the sample image data generates at least magnitudes; the means for calculating a fast fourier transform of the repair image data generates a phase and magnitudes; and the means for calculating an inverse fast fourier transform generates the new repair image data using a phase of the fast fourier transform data of the repair image data and one of the magnitudes of the fast fourier transform data of the sample image data and the magnitudes of the fast fourier transform data of the repair image data.
Claim 33
Dependent Claim
74 words
The apparatus of claim 29, further comprising: means for comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the means for performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 34
Dependent Claim
28 words
The apparatus of claim 30, wherein a magnitude of a DC component is selected as a magnitude of the fast fourier transform data of the repair image data.
Claim 35
Dependent Claim
18 words
The apparatus of claim 26, further comprising: means for conforming the repair image data to a predetermined value.
Claim 36
Dependent Claim
36 words
The apparatus of claim 26, further comprising: receiving the image data; a display for displaying the image data; and a memory for storing the soft noise mask, the repair image data and the sample image data.
Claim 37
Dependent Claim
34 words
The apparatus of claim 26, wherein the noise mask is binary data such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 38
Dependent Claim
56 words
The apparatus of claim 26, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 39
Dependent Claim
75 words
The apparatus of claim 35, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 40
Dependent Claim
57 words
The apparatus of claim 26, wherein the means for performing a frequency transform to the sample image data, the means for performing the frequency transform to the repair image data, the means for performing an inverse transform of the frequency transformed data and the means for generating new image data recursively operate to generate new image data.
Claim 41
Independent Claim
281 words
An apparatus for removing noise from image data, comprising: means for defining an area of noise on the image data; means for generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; means for defining sample image data which is similar to the repair image data; means for filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; means for performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; means for filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; means for performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; means for performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; means for combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; means for generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; and means for generating new image data using the new high pass filtered repair image data and the low pass filtered image data.
Claim 42
Independent Claim
281 words
An apparatus for removing noise from image data, comprising: means for defining an area of noise on the image data; means for generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; means for defining sample image data which is similar to the repair image data; means for filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; means for performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; means for filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; means for performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; means for performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; means for combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; means for generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; and means for generating new image data using the new high pass filtered repair image data and the low pass filtered image data.
Claim 43
Dependent Claim
51 words
The apparatus of claim 38, further comprising: means for creating from the noise mask a soft noise mask having a soft edge, wherein the means for generating new high pass filtered image data uses the new high pass filtered repair image data, the repair image data and the soft noise mask.
Claim 44
Dependent Claim
25 words
The apparatus of claim 38, further comprising: means for creating mask repair image data in accordance with the noise mask and the repair image data.
Claim 45
Dependent Claim
27 words
The apparatus of claim 39, further comprising the step of: creating mask repair image data in accordance with the soft noise mask and the repair image data.
Claim 46
Dependent Claim
135 words
The apparatus of claim 38, wherein the means for performing a frequency transform to the high pass filtered sample image data calculates a fast fourier transform of the high pass filtered sample image data to generate fast fourier transform data of the high pass filtered sample image data; the means for performing the frequency transform to the high pass filtered repair image data calculates a fast fourier transform of the high pass filtered repair image data to generate fast fourier transform data of the high pass filtered repair image data; and the means for performing an inverse transform of the frequency transform calculates an inverse fast fourier transform using the fast fourier transform data of the high pass filtered sample image data and the fast fourier transform data of the high pass repair image data.
Claim 47
Dependent Claim
131 words
The apparatus of claim 42, wherein the means for performing a frequency transform to the high pass filtered sample image data generates at least magnitudes of the fast fourier transform thereof; the means for performing the frequency transform to the high pass filtered repair image data generates a phase and magnitudes of the fast fourier transform thereof; and the means for performing an inverse transform of the frequency transform generates the new high pass filtered repair image data using a phase of the fast fourier transform data of the high pass filtered repair image data and one of the magnitudes of the fast fourier transform data of the high pass filtered sample image data and the magnitudes of the fast fourier transform data of the high pass filtered repair image data.
Claim 48
Dependent Claim
74 words
The apparatus of claim 43, further comprising: means for comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the means for performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 49
Dependent Claim
28 words
The apparatus of claim 44, wherein a magnitude of a DC component is selected as the magnitude of the fast fourier transform data of the repair image data.
Claim 50
Dependent Claim
22 words
The apparatus of claim 38, further comprising: means for conforming the new high pass filtered repair image data to a predetermined value.
Claim 51
Dependent Claim
36 words
The apparatus of claim 38, wherein the noise mask is binary data such that such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 52
Dependent Claim
56 words
The apparatus of claim 39, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 53
Dependent Claim
75 words
The apparatus of claim 48, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 54
Dependent Claim
112 words
The apparatus of claim 38, wherein the means for filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data, the means for performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data, and the means for performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data operate recursively to generate the new image data.
Claim 55
Dependent Claim
193 words
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for removing noise from image data, the method steps comprising: defining an area of noise on the image data; generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining repair image data including the defined area of noise; defining sample image data which is similar to the repair image data; creating from the noise mask a soft noise mask having a soft edge; performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; and generating new image data using the new repair image data, the repair image data and the soft noise mask.
Claim 56
Dependent Claim
193 words
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for removing noise from image data, the method steps comprising: defining an area of noise on the image data; generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining repair image data including the defined area of noise; defining sample image data which is similar to the repair image data; creating from the noise mask a soft noise mask having a soft edge; performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; and generating new image data using the new repair image data, the repair image data and the soft noise mask.
Claim 57
Dependent Claim
29 words
The program storage device of claim 51, wherein the method further comprises the step of: creating mask repair image data in accordance with data of the soft noise mask.
Claim 58
Dependent Claim
109 words
The program storage device of claim 51, wherein the step of performing a frequency transform to the sample image data calculates a fast fourier transform of the sample image data to generate fast fourier transform data of the sample image data; the step of performing a frequency transform to the repair image data calculates a fast fourier transform of the repair image data to generate fast fourier transform data of the repair image data; the step of performing an inverse transform calculates an inverse fast fourier transform using the fast fourier transform data of the sample image data and the fast fourier transform data of the repair image data.
Claim 59
Dependent Claim
103 words
The program storage device of claim 53, wherein the step of calculating a fast fourier transform of the sample image data generates at least magnitudes; the step of calculating a fast fourier transform of the repair image data generates a phase and magnitudes; and the step of calculating an inverse fast fourier transform generates the new repair image data using a phase of the fast fourier transform data of the repair image data and one of the magnitudes of the fast fourier transform data of the sample image data and the magnitudes of the fast fourier transform data of the repair image data.
Claim 60
Dependent Claim
80 words
The program storage device of claim 54, wherein the method further comprises the step of: comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the step of performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 61
Dependent Claim
30 words
The program storage device of claim 51, wherein a magnitude of a DC component is selected as a magnitude of the fast fourier transform data of the repair image data.
Claim 62
Dependent Claim
24 words
The program storage device of claim 51, wherein the method further comprises the step of: conforming the repair image data to a predetermined value.
Claim 63
Dependent Claim
38 words
The program storage device of claim 51, wherein the method further comprises the steps of: receiving the image data; displaying the image data; and storing the soft noise mask, the repair image data and the sample image data.
Claim 64
Dependent Claim
36 words
The program storage device of claim 51, wherein the noise mask is binary data such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 65
Dependent Claim
58 words
The program storage device of claim 51, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 66
Dependent Claim
77 words
The program storage device of claim 60, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 67
Dependent Claim
51 words
The program storage device of claim 51, wherein the steps of performing a frequency transform to the sample image data, performing the frequency transform to the repair image data, performing an inverse transform of the frequency transformed data and generating new image data are recursively performed to generate new image data.
Claim 68
Dependent Claim
282 words
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for removing noise from image data, the method steps comprising: defining an area of noise on the image data; generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining sample image data which is similar to the repair image data; filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; and generating new image data using the new high pass filtered repair image data and the low pass filtered image data.
Claim 69
Dependent Claim
282 words
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for removing noise from image data, the method steps comprising: defining an area of noise on the image data; generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining sample image data which is similar to the repair image data; filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; and generating new image data using the new high pass filtered repair image data and the low pass filtered image data.
Claim 70
Dependent Claim
57 words
The program storage device of claim 63, wherein the method further comprises the step of: creating from the noise mask a soft noise mask having a soft edge, wherein the step of generating new high pass filtered image data uses the new high pass filtered repair image data, the repair image data and the soft noise mask.
Claim 71
Dependent Claim
31 words
The program storage device of claim 63 wherein the method further comprises the step of: creating mask repair image data in accordance with the noise mask and the repair image data.
Claim 72
Dependent Claim
32 words
The program storage device of claim 64, wherein the method further comprises the step of: creating mask repair image data in accordance with the soft noise mask and the repair image data.
Claim 73
Dependent Claim
137 words
The program storage device of claim 63, wherein the step of performing a frequency transform to the high pass filtered sample image data calculates a fast fourier transform of the high pass filtered sample image data to generate fast fourier transform data of the high pass filtered sample image data; the step of performing the frequency transform to the high pass filtered repair image data calculates a fast fourier transform of the high pass filtered repair image data to generate fast fourier transform data of the high pass filtered repair image data; and the step of performing an inverse transform of the frequency transform calculates an inverse fast fourier transform using the fast fourier transform data of the high pass filtered sample image data and the fast fourier transform data of the high pass repair image data.
Claim 74
Dependent Claim
133 words
The program storage device of claim 67, wherein the step of performing a frequency transform to the high pass filtered sample image data generates at least magnitudes of the fast fourier transform thereof; the step of performing the frequency transform to the high pass filtered repair image data generates a phase and magnitudes of the fast fourier transform thereof; and the step of performing an inverse transform of the frequency transform generates the new high pass filtered repair image data using a phase of the fast fourier transform data of the high pass filtered repair image data and one of the magnitudes of the fast fourier transform data of the high pass filtered sample image data and the magnitudes of the fast fourier transform data of the high pass filtered repair image data.
Claim 75
Dependent Claim
80 words
The program storage device of claim 68, wherein the method further comprises the step of: comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the step of performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 76
Dependent Claim
30 words
The program storage device of claim 69, wherein a magnitude of a DC component is selected as the magnitude of the fast fourier transform data of the repair image data.
Claim 77
Dependent Claim
28 words
The program storage device of claim 63, wherein the method further comprises the step of: conforming the new high pass filtered repair image data to a predetermined value.
Claim 78
Dependent Claim
38 words
The program storage device of claim 63, wherein the noise mask is binary data such that such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 79
Dependent Claim
58 words
The program storage device of claim 64, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 80
Dependent Claim
77 words
The program storage device of claim 73, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 81
Dependent Claim
109 words
The program storage device of claim 60, wherein the steps of filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data, performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data, and performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data are performed recursively to generate the new image data.
Claim 82
Dependent Claim
193 words
A recording medium having digital data recorded thereon, the recording medium being prepared by the steps of: defining an area of noise on image data, the image data representative of an image; generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining repair image data including the defined area of noise; defining sample image data which is similar to the repair image data; creating from the noise mask a soft noise mask having a soft edge; performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; generating new image data using the new repair image data, the repair image data and the soft noise mask; and recording the new image data on the recording medium.
Claim 83
Dependent Claim
193 words
A recording medium having digital data recorded thereon, the recording medium being prepared by the steps of: defining an area of noise on image data, the image data representative of an image; generating a noise mask to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining repair image data including the defined area of noise; defining sample image data which is similar to the repair image data; creating from the noise mask a soft noise mask having a soft edge; performing a frequency transform to the sample image data and to generate frequency transformed data of the sample image data; performing the frequency transform to the repair image data to generate frequency transformed data of the repair image data; performing an inverse transform of the frequency transformed data of the sample image data and the frequency transformed data of the repair image data to generate new repair image data; generating new image data using the new repair image data, the repair image data and the soft noise mask; and recording the new image data on the recording medium.
Claim 84
Dependent Claim
25 words
The recording medium of claim 76, further comprising the step of: creating mask repair image data in accordance with data of the soft noise mask.
Claim 85
Dependent Claim
108 words
The recording medium of claim 76 wherein the step of performing a frequency transform to the sample image data calculates a fast fourier transform of the sample image data to generate fast fourier transform data of the sample image data; the step of performing a frequency transform to the repair image data calculates a fast fourier transform of the repair image data to generate fast fourier transform data of the repair image data; the step of performing an inverse transform calculates an inverse fast fourier transform using the fast fourier transform data of the sample image data and the fast fourier transform data of the repair image data.
Claim 86
Dependent Claim
102 words
The recording medium of claim 78, wherein the step of calculating a fast fourier transform of the sample image data generates at least magnitudes; the step of calculating a fast fourier transform of the repair image data generates a phase and magnitudes; and the step of calculating an inverse fast fourier transform generates the new repair image data using a phase of the fast fourier transform data of the repair image data and one of the magnitudes of the fast fourier transform data of the sample image data and the magnitudes of the fast fourier transform data of the repair image data.
Claim 87
Dependent Claim
76 words
The recording medium of claim 79, further comprising the step of: comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the step of performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 88
Dependent Claim
29 words
The recording medium of claim 80, wherein a magnitude of a DC component is selected as a magnitude of the fast fourier transform data of the repair image data.
Claim 89
Dependent Claim
20 words
The recording medium of claim 76, further comprising the step of: conforming the repair image data to a predetermined value.
Claim 90
Dependent Claim
34 words
The recording medium of claim 76, further comprising the steps of: receiving the image data; displaying the image data; and storing the soft noise mask, the repair image data and the sample image data.
Claim 91
Dependent Claim
35 words
The recording medium of claim 76, wherein the noise mask is binary data such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 92
Dependent Claim
57 words
The recording medium of claim 76, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 93
Dependent Claim
76 words
The recording medium of claim 85, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 94
Dependent Claim
50 words
The recording medium of claim 76, wherein the steps of performing a frequency transform to the sample image data, performing the frequency transform to the repair image data, performing an inverse transform of the frequency transformed data and generating new image data are recursively performed to generate new image data.
Claim 95
Dependent Claim
286 words
A recording medium having digital data recorded thereon, the recording medium being prepared by the steps of: defining an area of noise in an image represented by image data, the image data representative of an image; generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining sample image data which is similar to the repair image data; filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; generating new image data using the new high pass filtered repair image data and the low pass filtered image data; and recording the new image data on the recording medium.
Claim 96
Dependent Claim
286 words
A recording medium having digital data recorded thereon, the recording medium being prepared by the steps of: defining an area of noise in an image represented by image data, the image data representative of an image; generating noise mask data to distinguish pixels inside the defined area of noise on the image data from pixels outside the area of noise on the image data; defining sample image data which is similar to the repair image data; filtering the sample image data to generate low pass filtered sample image data and high pass filtered sample image data; performing a frequency transform to the high pass filtered sample image data to generate frequency transformed data of the high pass filtered sample image data; filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data; performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data; performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data; combining the low pass filtered repair image data and the low pass filtered sample image data to generate low pass filtered image data; generating new high pass filtered image data using the new high pass filtered repair image data, the repair image data and the noise mask; generating new image data using the new high pass filtered repair image data and the low pass filtered image data; and recording the new image data on the recording medium.
Claim 97
Dependent Claim
53 words
The recording medium of claim 88, further comprising the step of: creating from the noise mask a soft noise mask having a soft edge, wherein the step of generating new high pass filtered image data uses the new high pass filtered repair image data, the repair image data and the soft noise mask.
Claim 98
Dependent Claim
27 words
The recording medium of claim 88, further comprising the step of: creating mask repair image data in accordance with the noise mask and the repair image data.
Claim 99
Dependent Claim
28 words
The recording medium of claim 89, further comprising the step of: creating mask repair image data in accordance with the soft noise mask and the repair image data.
Claim 100
Dependent Claim
136 words
The recording medium of claim 88, wherein the step of performing a frequency transform to the high pass filtered sample image data calculates a fast fourier transform of the high pass filtered sample image data to generate fast fourier transform data of the high pass filtered sample image data; the step of performing the frequency transform to the high pass filtered repair image data calculates a fast fourier transform of the high pass filtered repair image data to generate fast fourier transform data of the high pass filtered repair image data; and the step of performing an inverse transform of the frequency transform calculates an inverse fast fourier transform using the fast fourier transform data of the high pass filtered sample image data and the fast fourier transform data of the high pass repair image data.
Claim 101
Dependent Claim
132 words
The recording medium of claim 92, wherein the step of performing a frequency transform to the high pass filtered sample image data generates at least magnitudes of the fast fourier transform thereof; the step of performing the frequency transform to the high pass filtered repair image data generates a phase and magnitudes of the fast fourier transform thereof; and the step of performing an inverse transform of the frequency transform generates the new high pass filtered repair image data using a phase of the fast fourier transform data of the high pass filtered repair image data and one of the magnitudes of the fast fourier transform data of the high pass filtered sample image data and the magnitudes of the fast fourier transform data of the high pass filtered repair image data.
Claim 102
Dependent Claim
76 words
The recording medium of claim 93, further comprising the step of: comparing the magnitudes of the fast fourier transform data of the sample image data with magnitudes and minimum magnitudes of the fast fourier transform data of the repair image data, wherein the step of performing an inverse transform generates the new image data using a selected one of the minimum magnitudes and the phase of the fast fourier transform data of the repair image data.
Claim 103
Dependent Claim
29 words
The recording medium of claim 94, wherein a magnitude of a DC component is selected as the magnitude of the fast fourier transform data of the repair image data.
Claim 104
Dependent Claim
24 words
The recording medium of claim 88, further comprising the step of: conforming the new high pass filtered repair image data to a predetermined value.
Claim 105
Dependent Claim
37 words
The recording medium of claim 88, wherein the noise mask is binary data such that such that it is a value of 1 wherever there is no noise and a value of 0 wherever there is noise.
Claim 106
Dependent Claim
57 words
The recording medium of claim 89, wherein the soft noise mask is soft edged data such that it is a value of 0 wherever there is noise, a value of between 0 and 1 near the edge of the noise and a value of 1 at at least a certain distance from the edge of the noise.
Claim 107
Dependent Claim
76 words
The recording medium of claim 98, wherein the value of the noise mask outside a noise area defined by 1-exp(-k×d×d), where k is a positive constant, and k is set to a large value where the noise mask is to have a broad soft edge, and k is set to a small value where the noise mask is to have a narrow soft edge, and where d is a distance from the edge of the noise.
Claim 108
Dependent Claim
108 words
The recording medium of claim 88, wherein the steps of filtering the repair image data to generate low pass filtered repair image data and high pass filtered repair image data, performing the frequency transform to the high pass filtered repair image data to generate frequency transformed data of the high pass filtered repair image data, and performing an inverse transform of the frequency transform using the frequency transformed data of the high pass filtered sample image data and the frequency transformed data of the high pass filtered repair image data to generate new high pass filtered repair image data are performed recursively to generate the new image data.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2002-04-05 | 2002-10-05 | 2003-04-06 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2006-04-05 | 2006-10-05 | 2007-04-06 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2010-04-05 | 2010-10-05 | 2011-04-06 | $7700.00 | Window Closed / Lapsed |