AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing

Abstract

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.

References in corpus (8)

• Message Passing Algorithms for Compressed Sensing
• Model-Based Compressive Sensing
• Learned Primal-dual Reconstruction
• Denoising Prior Driven Deep Neural Network for Image Restoration
• AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
• A Deep Learning Approach to Structured Signal Recovery
• The Cosparse Analysis Model and Algorithms
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Cited by in corpus (13)

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• Content-aware Scalable Deep Compressed Sensing
• Memory-Augmented Deep Unfolding Network for Compressive Sensing
• Image Compressed Sensing Using Non-local Neural Network
• AutoBCS: Block-based Image Compressive Sensing with Data-driven Acquisition and Non-iterative Reconstruction
• Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
• ADMM-DAD net: a deep unfolding network for analysis compressed sensing
• Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
• Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service
• Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction
• PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network
• WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm
• WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing