Channel Shuffle Reconstruction Network for Image Compressive Sensing

With the aim of improving the reconstruction quality for image compressive sensing, we propose a channel shuffle reconstruction network (CSRNet) by jointly optimize the sampling and the inverse reconstruction processes. Firstly, we build an initial reconstruction sub-network (IRSN) to adaptively learn the measurement matrix and generate a preliminary reconstructed image. Then, a deep channel shuffle sub-network (CSSN) is added to further improve the image quality. Specially, we combine the merits of the inverted residual structure with channel shuffle operation to propose an efficient channel shuffle block in CSSN. The inverted residual structure endows the network with more powerful feature extraction ability. The channel shuffle operation further promotes information interaction. Besides, we exploit the multi-scale convolution block to make better use of feature information at different scales. Experiments on the benchmark datasets demonstrate that the proposed network outperforms previous state-of-the-art algorithms with a comparable time complexity.