Zhang Zhonghao, Liu Yipeng, Liu Jiani, Wen Fei, Zhu Ce
IEEE Trans Image Process. 2021;30:1487-1500. doi: 10.1109/TIP.2020.3044472. Epub 2020 Dec 31.
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 article, 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.
大多数压缩感知（CS）重建方法可分为两类，即基于模型的方法和经典深度网络方法。通过将基于模型的方法的迭代优化算法展开到网络上，深度展开方法兼具基于模型的方法的良好可解释性和经典深度网络方法的高速度。在本文中，为了解决视觉图像CS问题，我们提出了一种名为AMP-Net的深度展开模型。它不是学习正则化项，而是通过展开著名的近似消息传递算法的迭代去噪过程来建立的。此外，AMP-Net集成了去块模块，以消除视觉图像CS中通常出现的块状伪影。另外，采样矩阵与其他网络参数联合训练以提高重建性能。实验结果表明，所提出的AMP-Net比其他现有最先进方法具有更好的重建精度，重建速度快且网络参数少。
