AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
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Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model based methods into networks, deep unfolding method has the good interpretation of model based methods and the high speed of classical deep network methods. In this paper, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net. Rather than learning regularization terms, it is established by unfolding the iterative denoising process of the well-known approximate message passing algorithm. Furthermore, AMP-Net integrates deblocking modules in order to eliminate the blocking artifact that usually appears in CS of visual image. In addition, the sampling matrix is jointly trained with other network parameters to enhance the reconstruction performance. Experimental results show that the proposed AMP-Net has better reconstruction accuracy than other state-of-the-art methods with high reconstruction speed and a small number of network parameters.
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