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.
翻译:多数压缩遥感(CS)重建方法可以分为两类,即基于模型的方法和传统的深层次网络方法。通过在网络上推出基于模型的方法的迭代优化算法,深层开发的方法能够很好地解释基于模型的方法和古典深层网络方法的高速。在本文中,为解决视觉图像 CS问题,我们建议了一个称为AMP-Net的深度开发模型。它不是学习正规化条件,而是通过推广众所周知的近似电文传递算法的迭代拆过程来建立。此外,AMP-Net整合了阻塞模块,以便消除通常出现在视觉图像中CS的阻塞性文物。此外,取样矩阵与其他网络参数共同培训,以加强重建绩效。实验结果表明,拟议的AMP-Net比其他重建速度高、网络参数少的先进方法的重建精度要强。