IMAGE RESTORATION CASCADE
First Claim
1. A computer-implemented method of restoring an image comprising:
- receiving, at a processor, a poor quality image;
applying the poor quality image to each of a plurality of trained machine learning predictors;
obtaining from each of the trained machine learning predictors, a restored version of the poor quality image.
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Accused Products
Abstract
Image restoration cascades are described, for example, where digital photographs containing noise are restored using a cascade formed from a plurality of layers of trained machine learning predictors connected in series. For example, noise may be from sensor noise, motion blur, dust, optical low pass filtering, chromatic aberration, compression and quantization artifacts, down sampling or other sources. For example, given a noisy image, each trained machine learning predictor produces an output image which is a restored version of the noisy input image; each trained machine learning predictor in a given internal layer of the cascade also takes input from the previous layer in the cascade. In various examples, a loss function expressing dissimilarity between input and output images of each trained machine learning predictor is directly minimized during training. In various examples, data partitioning is used to partition a training data set to facilitate generalization.
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Citations
20 Claims
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1. A computer-implemented method of restoring an image comprising:
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receiving, at a processor, a poor quality image; applying the poor quality image to each of a plurality of trained machine learning predictors; obtaining from each of the trained machine learning predictors, a restored version of the poor quality image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method of restoring an image comprising:
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receiving, at a processor, a poor quality image; applying the poor quality image to a plurality of trained machine learning predictors; obtaining at least one of a series of restored versions of the poor quality image from the trained machine learning predictors; displaying the obtained restored versions of the poor quality image and monitoring for user input; in response to user input, stopping application of the poor quality image to at least some of the plurality of trained machine learning predictors.
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14. An image restoration engine comprising:
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a processor arranged to receive a poor quality image; a plurality of trained machine learning predictors arranged to use the poor quality image to calculate restored versions of the poor quality image. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification