Abstract：Compressed sensing（CS），a technique for simultaneous data sampling and compression，can break through the limitation of traditional sampling theorem.It has become a popular research direction in the field of image processing that using deep learning method to solve the defects of CS algorithm.The existing algorithms under the deep framework mostly use afully-connected layer data sampling.For the natural images，the amount of computation is vary large，which is not conducive to the data storage.Block Compressed Sensing（BCS）methods mostly use single-scale block segmentation，and the selection of appropriate block size becomes a problem.In this paper，a deep learning method based on multi-scale block compressed sensing is proposed.The convolutional layer is used to replace the full-connected layerto realize multi-scale block segmentation of the original image，and the convolutional self-encoder is used to further optimize the reconstructed image.The experimental results show that the algorithm presented in this paper has obvious advantages in image festure extraction and reconstruction and achieved good results.
于洋,桑国明. 基于深度学习的多尺度分块压缩感知算法[J]. 小型微型计算机系统, 2020, 41(6): 1263-1268.
YU Yang,SANG Guo-ming. Multi-scale Block Compressed Sensing Based on Deep Learning. Journal of Chinese Computer Systems, 2020, 41(6): 1263-1268.
2020, Vol. 41 
