论文标题
FSOINET：用于图像压缩感测的功能空间优化灵感的网络
FSOINet: Feature-Space Optimization-Inspired Network for Image Compressive Sensing
论文作者
论文摘要
近年来，基于深度学习的图像压缩传感（ICS）方法取得了出色的成功。已经提出了许多优化灵感的网络，以将优化算法的见解带入网络结构设计中，并以低计算复杂性实现了出色的重建质量。但是，它们通过更新和传输像素空间中的图像来将信息流以传统算法的形式保持在像素空间中，而图像空间并未充分使用图像功能中的信息。在本文中，我们提出了在特征空间中按阶段实现信息流量的想法，并设计了一个功能空间优化启发的网络（称为FSOINET）来实现它，以通过映射从像素空间的近端梯度下降算法的两个步骤来实现它。此外，采样矩阵是通过其他网络参数端到端学习的。实验表明，所提出的FSOINET在定量和定性上都大大优于现有的最新方法。源代码可在https://github.com/cwjjun/fsoinet上找到。
In recent years, deep learning-based image compressive sensing (ICS) methods have achieved brilliant success. Many optimization-inspired networks have been proposed to bring the insights of optimization algorithms into the network structure design and have achieved excellent reconstruction quality with low computational complexity. But they keep the information flow in pixel space as traditional algorithms by updating and transferring the image in pixel space, which does not fully use the information in the image features. In this paper, we propose the idea of achieving information flow phase by phase in feature space and design a Feature-Space Optimization-Inspired Network (dubbed FSOINet) to implement it by mapping both steps of proximal gradient descent algorithm from pixel space to feature space. Moreover, the sampling matrix is learned end-to-end with other network parameters. Experiments show that the proposed FSOINet outperforms the existing state-of-the-art methods by a large margin both quantitatively and qualitatively. The source code is available on https://github.com/cwjjun/FSOINet.
