  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 onto
networks, deep unfolding methods have 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 artifacts that usually appear in CS of visual images.
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.
