Patent US6990252B2 successfully refreshed from Google Patents.
US6990252B2
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System for manipulating noise in digital images
Assignee: Adobe Inc
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Filing: 2001-05-23
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Granted: 2006-01-24
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Expires: 2021-05-23
Abstract
Abstract An apparatus for analyzing the broadband noise content of a digital image comprising the following: a means of automatically identifying regions of originally constant color in the image by analysis of the variance of pixel values of regions of the image; a means of automatically detecting and discarding regions deemed to be unrepresentative of the true noise content of an image, including under- and over-exposed regions; a means of allowing the user to manually select some or all required constant color regions if desired; and, a means of analyzing such constant color regions to generate a parametric or non-parametric model of the noise in the image, including frequency characteristic within and between channels and other characteristics such as phase which might describe structured noise.
Inventors
- 👤 Jonathan Martin Shekter
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 10:57
CPC Classifications
G; G06; G06T; G06T5/00; G06T5/10; G06T5/20; G06T5/70; G06V; G06V10/00; G06V10/20; G06V10/30; G06T2207/00; G06T2207/10; G06T2207/10024; G06T2207/20; G06T2207/20016; G06T2207/20076; G06T2207/20172; G06T2207/20204
Claims Analysis
Total Claims: 44
Claim 1
Independent Claim
201 words
A method of analyzing noise content in a digital image, the method comprising: identifying regions of approximately constant pixel value within the digital image; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSDs.
Claim 2
Independent Claim
201 words
A method of analyzing noise content in a digital image, the method comprising: identifying regions of approximately constant pixel value within the digital image; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSDs.
Claim 3
Dependent Claim
75 words
The method of claim 1 , wherein said identifying regions of approximately constant pixel value within the digital image comprises: defining a plurality of regions in the digital image; discarding any of said defined regions in which pixel values are saturated or clipped to generate a set of remaining regions; analyzing said remaining regions to determine variation among pixel values within each remaining region; and identifying said remaining regions having lower variation among pixel value.
Claim 4
Dependent Claim
21 words
The method of claim 2 , wherein said defining a plurality of regions comprises allowing a user to select said regions.
Claim 5
Dependent Claim
47 words
The method of claim 2 , wherein said defining a plurality of regions comprises: setting a region size for each of said plurality of regions; and using a random sampling pattern such that said plurality of regions include approximately all of the pixels in the digital image.
Claim 6
Dependent Claim
24 words
The method of claim 4 , wherein said region size for each of said plurality of regions is between 16 and 32 pixels square.
Claim 7
Dependent Claim
54 words
The method of claim 2 , wherein said discarding any of said defined regions in which pixel values are saturated or clipped comprises: computing the average and standard deviation of all pixels in all samples; and discarding those regions having an average value that is greater than one standard deviation from the overall average.
Claim 8
Dependent Claim
23 words
The method of claim 1 , wherein said identifying regions of approximately constant pixel value comprises allowing a user to select said regions.
Claim 9
Dependent Claim
19 words
The method of claim 1 , wherein said predetermined number of identified regions comprises between 5 and 10 regions.
Claim 10
Dependent Claim
16 words
The method of claim 1 , wherein said predetermined number of identified regions comprises 7 regions.
Claim 11
Dependent Claim
61 words
The method of claim 1 , wherein said analyzing the power spectral density of the noise in said selected regions comprises: applying an auto correlation function (ACF) model to the noise present in each selected region; and applying a discrete Fourier transform to the ACF model for each selected region to obtain a PSD for the noise in each selected region.
Claim 12
Dependent Claim
25 words
The method of claim 1 , wherein the pixel value is a vector that includes multiple values or channels and each channel is analyzed independently.
Claim 13
Dependent Claim
20 words
The method of claim 11 , further comprising using a decorrelating transform to uncorrelate any channels that are initially correlated.
Claim 14
Dependent Claim
27 words
The method of claim 1 , wherein the pixel value is a vector that includes multiple values or channels and each channel is analyzed independently and simultaneously.
Claim 15
Dependent Claim
23 words
The method of claim 1 , wherein the digital image comprises a plurality of frames and each frame is analyzed independently and simultaneously.
Claim 16
Dependent Claim
27 words
The method of claim 1 , further comprising performing a homomorphic transformation on each pixel value prior to the analysis and a reverse transformation following the analysis.
Claim 17
Dependent Claim
218 words
A computer readable medium containing computer executable instructions for analyzing noise content in a digital image, which, when operating in a processor cause the processor to perform the functions of: identifying regions of approximately constant pixel value within the digital image; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSDs.
Claim 18
Dependent Claim
218 words
A computer readable medium containing computer executable instructions for analyzing noise content in a digital image, which, when operating in a processor cause the processor to perform the functions of: identifying regions of approximately constant pixel value within the digital image; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSDs.
Claim 19
Dependent Claim
77 words
The computer readable medium of claim 16 , wherein said identifying regions of approximately constant pixel value within the digital image comprises: defining a plurality of regions in the digital image; discarding any of said defined regions in which pixel values are saturated or clipped to generate a set of remaining regions; analyzing said remaining regions to determine variation among pixel value within each remaining region; and identifying said remaining regions having lower variation among pixel value.
Claim 20
Dependent Claim
56 words
The computer readable medium of claim 17 , wherein said defining a plurality of regions comprises: setting a region size of between 16 and 32 pixels square for each of said plurality of regions; and using a random sampling pattern such that said plurality of regions include approximately all of the pixels of the digital image.
Claim 21
Dependent Claim
56 words
The computer readable medium of claim 17 , wherein said discarding any of said defined regions in which pixel values are saturated or clipped comprises: computing the average and standard deviation of all pixels in all samples; and discarding those regions having an average value that is greater than one standard deviation from the overall average.
Claim 22
Dependent Claim
21 words
The computer readable medium of claim 16 , wherein said predetermined number of identified regions comprises between 5 and 10 regions.
Claim 23
Dependent Claim
63 words
The computer readable medium of claim 16 , wherein said analyzing the power spectral density of the noise in said selected regions comprises: applying an auto correlation function (ACF) model to the noise present in each selected region; and applying a discrete Fourier transform to the ACF model for each selected region to obtain a PSD for the noise in each selected region.
Claim 24
Dependent Claim
27 words
The computer readable medium of claim 16 , wherein the pixel value is a vector that includes multiple values or channels and each channel is analyzed independently.
Claim 25
Dependent Claim
239 words
A software program product for use with a processor, the software program product comprising processor readable program code for analyzing noise content in a digital image, wherein when said processor readable program code is executed in a processor, said processor readable program code causes said processor to perform the functions of: identifying regions of approximately constant pixel value within the digital image; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSI)s.
Claim 26
Dependent Claim
239 words
A software program product for use with a processor, the software program product comprising processor readable program code for analyzing noise content in a digital image, wherein when said processor readable program code is executed in a processor, said processor readable program code causes said processor to perform the functions of: identifying regions of approximately constant pixel value within the digital image; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSI)s.
Claim 27
Dependent Claim
77 words
The software program product of claim 23 , wherein said identifying regions of approximately constant pixel value within the digital image comprises: defining a plurality of regions in the digital image; discarding any of said defined regions in which pixel values are saturated or clipped to generate a set of remaining regions; analyzing said remaining regions to determine variation among pixel value within each remaining region; and identifying said remaining regions having lower variation among pixel value.
Claim 28
Dependent Claim
49 words
The software program product of claim 24 , wherein said defining a plurality of regions comprises: setting a region size for each of said plurality of regions; and using a random sampling pattern such that said plurality of regions include approximately all of the pixels in the digital image.
Claim 29
Dependent Claim
26 words
The software program product of claim 25 , wherein said region size for each of said plurality of regions is between 16 and 32 pixels square.
Claim 30
Dependent Claim
56 words
The software program product of claim 24 , wherein said discarding any of said defined regions in which pixel values are saturated or clipped comprises: computing the average and standard deviation of all pixels in all sample, and discarding those regions having an average value that is greater than one standard deviation from the overall average.
Claim 31
Dependent Claim
25 words
The software program product of claim 23 , wherein said identifying regions of approximately constant pixel value comprise allowing a user to select said regions.
Claim 32
Dependent Claim
21 words
The software program product of claim 23 , wherein said predetermined number of identified regions comprises between 5 and 10 regions.
Claim 33
Dependent Claim
18 words
The software program product of claim 23 , wherein said predetermined number of identified regions comprises 7 regions.
Claim 34
Dependent Claim
63 words
The software program product of claim 23 , wherein said analyzing the power spectral density of the noise in said selected regions comprises: applying an auto correlation function (ACF) model to the noise present in each selected region; and applying a discrete Fourier transform to the ACF model for each selected region to obtain a PSD for the noise in each selected region.
Claim 35
Dependent Claim
63 words
The software program product of claim 23 , wherein said analyzing the power spectral density of the noise in said selected regions comprises: applying an auto correlation function (ACF) model to the noise present in each selected region; and applying a discrete Fourier transform to the ACF model for each selected region to obtain a PSD for the noise in each selected region.
Claim 36
Dependent Claim
27 words
The software program product of claim 23 , wherein the pixel value is a vector that includes multiple values or channels and each channel is analyzed independently.
Claim 37
Dependent Claim
22 words
The software program product of claim 33 , further comprising using a decorrelating transform to uncorrelate any channels that are initially correlated.
Claim 38
Dependent Claim
29 words
The software program product of claim 23 , wherein the pixel value is a vector that includes multiple values or channels and each channel is analyzed independently and simultaneously.
Claim 39
Dependent Claim
29 words
The software program product of claim 23 , further comprising: performing a homomorphic transformation on each pixel value prior to the analysis and a reverse transformation following the analysis.
Claim 40
Independent Claim
260 words
A method of analyzing noise content in a digital image, the method comprising: identifying regions of approximately constant pixel value within the digital image, wherein said identifying comprises: defining a plurality of regions in the digital image; discarding any of said defined regions in which pixel values are saturated or clipped to generate a set of remaining regions; analyzing said remaining regions to determine variation among pixel value within each remaining region; and identifying said remaining regions having lower variation among pixel value; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSDs.
Claim 41
Independent Claim
260 words
A method of analyzing noise content in a digital image, the method comprising: identifying regions of approximately constant pixel value within the digital image, wherein said identifying comprises: defining a plurality of regions in the digital image; discarding any of said defined regions in which pixel values are saturated or clipped to generate a set of remaining regions; analyzing said remaining regions to determine variation among pixel value within each remaining region; and identifying said remaining regions having lower variation among pixel value; selecting a predetermined number of said identified regions based on pixel value variances within each said identified region; analyzing said selected regions to generate a mathematical model of noise present in the digital image, wherein said analyzing said selected regions to generate a mathematical model of the noise present in the digital image comprises: processing each selected region to remove a mean pixel value from each pixel in the selected region and obtain an approximation of the noise in each selected region, analyzing the power spectral density (PSD) of the noise in said selected regions, generating a parametric model of the noise present in the digital image based on said PSD of the noise in said selected regions, wherein said generating a parametric model of the noise present in the digital image based on said PSD for each selected region comprises: taking a discrete Fourier transform of each noise sample, and squaring each resulting frequency component to produce a periodogram, averaging together the periodograms; and selecting a best-fit linear combination of a small set of predefined PSDs.
Claim 42
Dependent Claim
47 words
The method of claim 37 , wherein said defining a plurality of regions comprises: setting a region size for each of said plurality of regions; and using a random sampling pattern such that said plurality of regions include approximately all of the pixels in the digital image.
Claim 43
Dependent Claim
19 words
The method of claim 37 , wherein said predetermined number of identified regions comprises between 5 and 10 regions.
Claim 44
Dependent Claim
61 words
The method of claim 37 , wherein said analyzing the power spectral density of the noise in said selected regions comprises: applying an auto correlation function (ACF) model to the noise present in each selected region; and applying a discrete Fourier transform to the ACF model for each selected region to obtain a PSD for the noise in each selected region.
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