Image Compressed Sensing Recovery Via Adaptive Dictionary Learning

This paper addresses the image compressed sensing recovery problem. To improve the recovery quality, instead of using a fixed dictionary that is generally a universal one trained in an off-line manner for sparse representations of image patches, we adopt an adaptive dictionary learning strategy. Inspired by the monotone fast iterative shrinkage-thresholding algorithm, a dictionary learning algorithm is introduced in this work. Also, we abandon the classic method that breaks an image into fully overlapping patches, and propose a new overlapping patches extraction method, which decreases the number of patches and saves much run-time, while achieves similar recovery qualities.