• Corpus ID: 11869289

# A Deep Learning Approach to Block-based Compressed Sensing of Images

title={A Deep Learning Approach to Block-based Compressed Sensing of Images},
author={Amir Adler and David Boublil and Michael Elad and Michael Zibulevsky},
journal={ArXiv},
year={2016},
volume={abs/1606.01519}
}
• Published 5 June 2016
• Computer Science
• ArXiv
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…

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