AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
This work addresses visual image compressive sensing, offering improved reconstruction for applications like imaging, but it is incremental as it builds on existing deep unfolding and denoising techniques.
The paper tackled the compressive image sensing reconstruction problem by proposing AMP-Net, a deep unfolding model based on iterative denoising, which achieved better reconstruction accuracy than state-of-the-art methods with high speed and fewer parameters.
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.