Open AccessDL-CSNet: Dictionary Learning based Compressed Sensing Neural Network
Author(s) -
Yanzhen Qiu,
Chuangfeng Zhang,
Ruishan Huang,
Haochen Tian,
Chenkui Xiong,
Shaolin Liao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2245/1/012015
Subject(s) - computer science , smoothing , artificial neural network , layer (electronics) , complement (music) , compressed sensing , artificial intelligence , pattern recognition (psychology) , binary number , constraint (computer aided design) , simple (philosophy) , matrix (chemical analysis) , deep learning , algorithm , computer vision , mathematics , arithmetic , biochemistry , chemistry , philosophy , geometry , organic chemistry , epistemology , materials science , complementation , composite material , gene , phenotype
In this paper, we propose a novel neural network for Compressed Sensing (CS) application: the Dictionary Learning based Compressed Sensing neural Network (DL-CSNet). It is fairly simple but highly effective, which consists of only three layers: 1) a DL layer for latent sparse features extraction; 2) a smoothing layer via Total Variation (TV) like constraint; and 3) a CS acquisition layer for neural network training. In particular, the TV-like smoothing layer is a perfect complement to the sparsity-oriented DL layer to achieve smooth images. The trained DL-CSNet can learn the optimal dictionary matrix so that images can be reconstructed in high quality. At last, extensive experiments have been carried out on binary images and compared to most classical CS algorithms, which shows the superior performance of the proposed DL-CSNet.