Abstract: There are two main problems in traditional Image Compressive Sensing (ICS): in sampling aspect，traditional linear sampling methods have some limitations; in reconstruction aspect，optimization-based reconstruc-tion methods are highly time-consuming． Newly proposed ICS Neural Network can successfully deal with the speed problem in reconstruction，but lacks the accuracy of traditional algorithms． To solve this problem，a novel multi-stage ICS network based on residual learning (MSResICS) was proposed． It consists of three sub-networks，namely，sampling sub-network，initial reconstruction sub-network，and image enhancement sub-network． In sampling stage，with the help of residual learning，a nonlinear sampling sub-network was designed，which breaks the limitation of conventional sampling method and retains richer image information in samples． In reconstruction stage，the initial reconstruction sub-network extracts features from samples and obtain an initial reconstructed image of high quality by introducing interpolation convolution． With residual learning and interpolation convolution，a mul-tistage image enhancing sub-network was proposed to further refine the reconstruction image and improve the quality of final result． Extensive simulations show that MSResICS has a better reconstruction accuracy than the existing opti-mal ICS reconstruction methods．

Key words: image compressive sensing, residual learning, deep network, sampling mechanism