# A Deep Learning Approach to Block-based Compressed Sensing of Images Source: [A Deep Learning Approach to Block-based Compressed Sensing of Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:21:33.904Z Authors: Amir Adler, David Boublil, Michael Elad, Michael Zibulevsky Published: 2016-06-05T14:40:54.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [CS Reconstruction on Set5 (test)](): CS reconstruction; Set5 (test); PSNR: 30.01. - [CS reconstruction on Set14](): CS reconstruction; Set14; PSNR: 27.33. - [CS reconstruction on Set14 (test)](): CS reconstruction; Set14 (test); PSNR: 27.33. ## Original sources and implementation [Original paper]() [PDF]()