Compressed Sensing Image Reconstruction Based on Convolutional Neural Network

DOI

10.2991/ijcis.d.190808.001How to use a DOI?

Keywords

Image reconstruction; Compressed sensing; CNN; Reconstruction accuracy; PSNR

Abstract

Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity, resource saving, and strong anti-interference ability. Although the compression sensing theory solves the problems brought by the traditional signal processing methods to a certain extent, it also encounters some new problems: the reconstruction time is long and the algorithm complexity is high. In order to solve these problems and further improve the quality of image processing, a new convolutional neural network structure CombNet is proposed, which uses the measured value of compression sensing as the input of the convolutional neural network, and connects a complete connection layer to get the final Output. Experiments show that CombNet has lower complexity and better recovery performance. At the same sampling rate, the peak signal-to-noise ratio (PSNR) is 12.79%–52.67% higher than Tval3 PSNR, 16.31%–158.37% higher than D-AMP, 1.00%–3.79% higher than DR²-Net, and 0.06%–2.60% higher than FCMN. It still has good visual appeal when the sampling rate is very low (0.01).

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yuhong Liu
AU  - Shuying Liu
AU  - Cuiran Li
AU  - Danfeng Yang
PY  - 2019
DA  - 2019/08/19
TI  - Compressed Sensing Image Reconstruction Based on Convolutional Neural Network
JO  - International Journal of Computational Intelligence Systems
SP  - 873
EP  - 880
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190808.001
DO  - 10.2991/ijcis.d.190808.001
ID  - Liu2019
ER  -