Abstract: Compressed sensing(CS) is a signal processing framework for effectively reconstructing signal from a small number of measurements obtained by linear projections of the signal.It’s an ongoing challenge for the practicality of computational imaging based on CS.The improvement of image reconstruction model is to incorporate more prior constraints under the signal sparse constraint,and the iterative optimization process is complex and time-consuming.Neural networks,as the application models of deep learning,can realize the approximation of any complex function,which provides a new technical route for high-quality and real-time image reconstruction.In this paper,deep neural network(DNN) is used for reconstruction and the block processing is used to reduce the reconstruction time and the number of network nodes,which avoids the complicated algorithm solving process of CS.The DNN model is obtained by training a large number of different types of images,and then the block CS measurement and DNN nonlinear solution are combined jointly to achieve efficient reconstruction.Experimental results show that,compared with six different reconstruction algorithms,the peak signal-to-noise ratio(PSNR) and structure similarity(SSIM) of images are improved in different degrees.Compared with the advanced CS algorithm,not only the reconstruction quality is comparable,but also the time complexity of DNN is greatly reduced and the reconstruction time is less than 3s.When sampling rate is as low as 0.01,the proposed approach can still reconstruct the image clearly while other algorithms fail.When sampling rate is 0.1,compared with the recent residual network method,the maximum(minimum) gain of PSNR is 6.7(1.97) dB,and the maximum(minimum) gain of SSIM is 0.354(0.122).

Key words: Image processing, Block-based compressed sensing, Reconstruction algorithm, Deep learning, Neural network