基于多尺度残差网络的压缩感知重构算法

目前压缩感知系统利用少量测量值使用迭代优化算法重构图像.在重构过程中，迭代重构算法需要进行复杂的迭代运算和较长的重构时间.本文提出了多尺度残差网络结构，利用测量值通过网络重构出图像.网络中引入多尺度扩张卷积层用来提取图像中不同尺度的特征，利用这些特征信息重构高质量图像.最后，将网络的输出与测量值进行优化，使得重构图像在测量矩阵上的投影与测量值更加接近.实验结果表明，本文算法在重构质量和重构时间上均有明显优势.

A Compressed Sensing Algorithm Based on Multi-scale Residual Reconstruction Network

In recent years, a small number of measurements and iterative optimization algorithms were exploited in compressed sensing to reconstruct images. In the process of reconstruction, most algorithms based on iteration for compressed sensing image reconstruction suffer from the complicatedly iterative computation and time-consuming. In this paper, we propose a novel multi-scale residual reconstruction network (MSRNet), and exploit the measurements to reconstruct images through the network. The multi-scale dilate convolution layer is introduced in the network to extract the feature of different scales in the image, and the feature information could improve the quality of reconstructed image. Finally, we exploit the output of the network and measurements to optimize our algorithm, so as to make the projection of the reconstructed image closer to the measurements. The experimental results show that the MSRNet requires less running time and has better performance in reconstruction quality than other compressed sensing algorithms.