loading page

DL-CSNet: Dictionary Learning based Compressed Sensing Neural Network
  • +4
  • Yanzhen Qiu ,
  • Chuangfeng Zhang ,
  • Yuhang Xie ,
  • Ruishan Huang ,
  • Haochen Tian ,
  • Chenkui Xiong ,
  • Shaolin Liao
Yanzhen Qiu
Author Profile
Chuangfeng Zhang
Author Profile
Yuhang Xie
Author Profile
Ruishan Huang
Author Profile
Haochen Tian
Author Profile
Chenkui Xiong
Author Profile
Shaolin Liao
Sun Yat-sen University, Sun Yat-sen University

Corresponding Author:[email protected]

Author Profile

Abstract

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.
01 Apr 2022Published in Journal of Physics: Conference Series volume 2245 issue 1 on pages 012015. 10.1088/1742-6596/2245/1/012015