论文标题
AMP-NET：基于DeNoing的深层展开，以进行压缩图像传感
AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
论文作者
论文摘要
大多数压缩传感（CS）重建方法可以分为两类，即基于模型的方法和经典深层网络方法。通过将基于模型方法的迭代优化算法展开到网络上，深层展开的方法可以很好地解释基于模型的方法和经典深层网络方法的高速。在本文中，为了解决视觉图像CS问题，我们提出了一个称为AMP-NET的深层展开模型。它不是通过学习正则化术语，而是通过展开众所周知的近似消息传递算法的迭代授权过程。此外，AMP-NET集成了排除模块，以消除通常出现在视觉图像CS中的阻塞伪像。此外，采样矩阵与其他网络参数共同训练，以增强重建性能。实验结果表明，所提出的AMP-NET比具有高重建速度和少量网络参数的其他最先进的方法具有更好的重建精度。
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.
