A Deep Learning Approach to Block based Compressed Sensing of Images
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block based CS is a lightweight CS approach that is mostly suitable for processing very high dimensional images and videos: it operates on local patches, employs a low complexity reconstruction operator and requires significantly less memory to store the sensing matrix. In this paper we present a deep learning approach for block based CS, in which a fully connected network performs both the block based linear sensing and non linear reconstruction stages. During the training phase, the sensing matrix and the non linear reconstruction operator are \emph{jointly} optimized, and the proposed approach outperforms state of the art both in terms of reconstruction quality and computation time. For example, at a 25% sensing rate the average PSNR advantage is 0.77dB and computation time is over 200 times faster.
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