EP0512422A2
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EP
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Method for temporally adaptive filtering of noisy image sequences
Assignee: Eastman Kodak Co
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Filing: 1992-04-30
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Granted: —
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Expires: 2012-04-30
Abstract
Abstract A method of temporally adaptive filtering of the frames of an image sequence is disclosed. To filter the kth frame, first, the motion trajectories that transverse the pixel locations at the kth frame are determined using a motion estimation algorithm. Motion trajectories pass through a predetermined number of frames neighboring the kth frame. Image values at the neighboring frames, along motion trajectories are then evaluated. Temporally adaptive linear minimum mean square error (LMMSE) estimates of the image values at the pixel locations of the kth frame are determined using the image values along the motion trajectories. These steps are repeated for each of the other frames of the image sequence.
Inventors
- 👤 M. Ibrahim C/O Eastman Kodak Company Sezan
- 👤 Mehmet c/o EASTMAN KODAK COMPANY Ozkan
- 👤 Sergei V. C/O Eastman Kodak Company Fogel
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-11-08 13:22
CPC Classifications
G; G06; G06T; G06T7/00; G06T7/20; H; H04; H04N; H04N19/00; H04N19/50; H04N19/503; H04N19/51; H04N19/60; H04N19/61; H04N19/615; H04N19/63; H04N19/80; H04N5/00; H04N5/14; H04N5/21; H04N19/10; H04N19/102; H04N19/13
Claims Analysis
Total Claims: 18
Claim 1
Independent Claim
88 words
A method of temporally adaptive filtering frames of an image sequence in which each image of the sequence has pixel locations, comprising: (a) determining motion trajectories that transverse the pixel locations at a kth frame using a motion estimation algorithm; (b) evaluating image values at the motion trajectories; (c) determining temporally adaptive linear minimum mean square error (LMMSE) estimates of image values of the pixel locations at the kth frame; and (d) repeating steps a, b and c for each of the other frames of the image sequence.
Claim 2
Dependent Claim
52 words
The method of Claim 1, wherein, for a pixel located at (m,n) in the kth frame, the motion trajectory τ m,n,k is defined as a set of locations containing (m,n) as well as displaced locations corresponding to (m,n) in N - 1 neighboring frames at times k-M, ... k-1, k+1, ..., k+M.
Claim 3
Dependent Claim
16 words
The method of Claim 1, further comprising (e) estimating displacement vectors with the motion estimation algorithm.
Claim 4
Dependent Claim
34 words
The method of Claim 3, wherein components of the displacement vector estimates are real-valued, such that the trajectories include subpixel locations, and further comprising (f) determining image values at the subpixel locations by interpolation.
Claim 5
Dependent Claim
14 words
The method of Claim 1, wherein the motion estimation algorithm is a spatiotemporal-gradient method.
Claim 6
Dependent Claim
13 words
The method of Claim 5, wherein the spatiotemporal-gradient method is the Fogel algorithm.
Claim 7
Dependent Claim
52 words
The method of Claim 1, wherein step (a) includes the steps of: (a)(1) forming a multiresolution representation of the image sequence; (a)(2) constraining a displacement vector field estimate using optical flow and directional smoothness constraints; (a)(3) obtaining a displacement vector field estimate using a variational principle; and (a)(4) solving the variational principle.
Claim 8
Dependent Claim
83 words
The method of Claim 1, wherein the temporally local LMMSE estimate of the image value at pixel location (m,n) of the kth frame is determined according to the equation: where σ̂ f ²(m,n;k) = max[0,σ̂ g ²(m,n;k)-σ̂ v ²(m,n;k)], and σ̂ g ²(m,n;k) is the sample variance, and µ̂ f (m,n;k) = µ̂ g (m,n;k) is the sample mean of a degraded image sequence computed over the motion trajectory τ m,n,k and σ̂ v ²(m,n) denotes the sample variance of a noise process.
Claim 9
Dependent Claim
28 words
The method of Claim 1, wherein step (a) includes estimating the motion trajectories from the kth frame to a predetermined number of contiguous frames in the image sequence.
Claim 10
Dependent Claim
19 words
The method of Claim 9, wherein the predetermined number of contiguous frames are used to filter the kth frame.
Claim 11
Dependent Claim
25 words
The method of Claim 1, wherein step (a) includes estimating the motion trajectories from the kth frame to a k-1 frame and a k+1 frame.
Claim 12
Dependent Claim
37 words
The method of Claim 11, wherein the k-1, k and k+1 frames are filtered to produce three intermediate frames, these intermediate frames then being filtered to determine an estimate f̂(m,n,k) of the image value in step (c).
Claim 13
Dependent Claim
47 words
The method of Claim 1, wherein the kth frame is filtered using N = 2M + 1 frames, the filtering being performed in M steps, and the LMMSE estimation of step (c) is applied to groups of three frames where N is a predetermined number of frames.
Claim 14
Dependent Claim
96 words
The method of Claim 1, wherein the temporally local LMMSE estimate of the image value at pixel location (m,n) of the kth frame is determined according to the equation: where σ̂ f ²(m,n;k) = max[0,σ̂ g ²(m,n;k)-σ̂ v ²(m,n;k)], and σ g ²(m,n;k) is the sample variance, and µ̂ a f (m,n;k) = is a motion-adaptive sample mean where the weight function is given by w(i,j,l) ≐ K (1 + a²max[ε², (g(m,n,k) - g(i,j,l))²])⁻¹, and the constant K is defined as the quantity β being given by, β(i,j,l) ≐ 1 + a²(max[ε² , (g(m,n,k) - g(i,j,l))²]).
Claim 15
Dependent Claim
53 words
The method of Claim 1, wherein the temporally local LMMSE estimate of the image value at pixel location (m,n) of the kth frame is determined according to the equation f̂(m,n,k) = µ̂ a f (m,n,k) where µ̂ a f (m,n,k) is a motion-adaptive sample mean value and f̂(m,n,k) is a filtered pixel value.
Claim 16
Independent Claim
82 words
A method of temporally adaptively filtering frames of an image sequence in which each image of the sequence has pixel locations, comprising: (a) determining motion trajectories that transverse the pixel locations at a kth frame using a motion estimation algorithm; (b) evaluating image values at the motion trajectories; (c) determining temporally adaptive estimates of image values of the pixel locations at the kth frame; and (d) repeating steps a, b and c for each of the other frames of the image sequence.
Claim 17
Dependent Claim
20 words
The method of Claim 16, wherein step (c) includes median filtering along the motion trajectories to perform temporal noise filtering.
Claim 18
Dependent Claim
12 words
The method of Claim 16, wherein step (c) includes edge-preserving spatial filtering.
References Cited (Backward Citations)
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