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The theory of compressed sensing (CS) has been successfully applied to imagecompression in the past few years, whose traditional iterative reconstructionalgorithm is time-consuming. However, it has been reported deep learning-basedCS reconstruction algorithms could greatly reduce the computational complexity.In this paper, we propose two efficient structures of cascaded reconstructionnetworks corresponding to two different sampling methods in CS process. Thefirst reconstruction network is a compatibly sampling reconstruction network(CSRNet), which recovers an image from its compressively sensed measurementsampled by a traditional random matrix. In CSRNet, deep reconstruction networkmodule obtains an initial image with acceptable quality, which can be furtherimproved by residual network module based on convolutional neural network. Thesecond reconstruction network is adaptively sampling reconstruction network(ASRNet), by matching automatically sampling module with corresponding residualreconstruction module. The experimental results have shown that the proposedtwo reconstruction networks outperform several state-of-the-art compressivesensing reconstruction algorithms. Meanwhile, the proposed ASRNet can achievemore than 1 dB gain, as compared with the CSRNet.

Cascaded Reconstruction Network for Compressive image sensing