Despite the remarkable progress has made in deep compressed sensing (DCS), how to improve the reconstruction quality is still a major challenge. The existing DCS model generally still has some issues, especially in recovering details. In this paper, a new parallel enhanced network (PENet) is proposed for image compressed sensing. PENet is designed as a sampling network and a parallel network, which contains a basic network and an enhanced network. The basic network is designed to provide the initial reconstructed image. The enhanced network is trained to progressively acquire module details through the connections with each block of the basic network in stages. The final reconstructed image is the cumulative results between the parallel network. Experimental result shows that PENet has a high reconstruction quality and comparable running time complexity with existing advanced DCS methods.
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