Image restoration cascade

摘要

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.

主权项

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 which are arranged in a cascade architecture, the cascade architecture comprising a plurality of layers connected in series, each layer comprising at least one of the trained machine learning predictors, each internal layer of the cascade architecture being trained using more training data examples than the first layer of the cascade architecture; obtaining from each of the trained machine learning predictors, a restored version of the poor quality image.