Recursive conditional means image denoising
First Claim
1. A method of estimating an original value of a pixel in a digital image, said method comprising:
- (i) identifying noise in a high quality digital image and producing a digital image noise model;
(ii) adding model noise based on said digital image noise model to a plurality of ground-truth digital images and producing a plurality of training images;
(iii) recursively measuring and applying conditional means to a plurality of different local pixel contexts based on said training images and producing a look-up table comprising estimates of point (pixel) values;
(iv) terminating the recursive measuring and applying conditional means to a plurality of different local pixel contexts based on said training images when the convergence of the conditional means indicates an asymptote; and
(v) assigning a value to a pixel in a digital image using said estimates of point (pixel) values in said look-up table.
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Accused Products
Abstract
Methods and composition for denoising digital camera images are provided herein. The method is based on directly measuring the local statistical structure of natural images in a large training set that has been corrupted with noise mimicking digital camera noise. The measured statistics are conditional means of the ground truth pixel value given a local context of input pixels. Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context. The conditional means are measured and applied recursively (e.g., the second conditional mean is measured after denoising with the first conditional mean). Each local context vector consists of only three variables, and hence the conditional means can be measured directly without prior assumptions about the underlying probability distributions, and they can be stored in fixed lookup tables.
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Citations
20 Claims
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1. A method of estimating an original value of a pixel in a digital image, said method comprising:
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(i) identifying noise in a high quality digital image and producing a digital image noise model; (ii) adding model noise based on said digital image noise model to a plurality of ground-truth digital images and producing a plurality of training images; (iii) recursively measuring and applying conditional means to a plurality of different local pixel contexts based on said training images and producing a look-up table comprising estimates of point (pixel) values; (iv) terminating the recursive measuring and applying conditional means to a plurality of different local pixel contexts based on said training images when the convergence of the conditional means indicates an asymptote; and (v) assigning a value to a pixel in a digital image using said estimates of point (pixel) values in said look-up table. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for estimating an original value of a pixel in a digital image, said system comprising:
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a processor configured to; identify noise in a high quality digital image and produce a digital image noise model; add model noise based on said digital image noise model to a plurality of ground-truth digital images and produce a plurality of training images; recursively measure and apply conditional means for a plurality of different local pixel contexts based on said training images and produce a look-up table comprising estimates of point (pixel) values; terminate the recursive measuring and applying conditional means to a plurality of different local pixel contexts based on said training images when the convergence of the conditional means indicates an asymptote; and assign a value to a pixel in a digital image using said estimates of point (pixel) values in said look-up table; and memory configured to store said look-up table. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A method of generating a denoised image comprising:
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(a) receiving at least one input image at a processor; (b) identifying a plurality of denoising contexts within the at least one input image, wherein the denoising contexts comprise a location and selected areas proximate to the location; (c) identifying a Bayes optimal estimate, wherein identifying the Bayes optimal estimate comprises measuring local statistics within the plurality of denoising contexts, wherein the local statistics comprise at least one of a conditional mean and a conditional variance based on the measured local statistics; (d) generating at least one next image based on the Bayes optimal estimate; (e) identifying a plurality of next image denoising contexts within the at least one next image, wherein the denoising contexts comprise a location and selected areas proximate to the location; (f) measuring next image local statistics within the plurality of next image denoising contexts, wherein the next image local statistics comprise at least one of a conditional mean and a conditional variance; and (g) determining whether the next image local statistics and the local statistics indicate an asymptote. - View Dependent Claims (18, 19, 20)
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Specification