论文标题
LR-CSNET：用于图像压缩传感的低级别深度展开网络
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing
论文作者
论文摘要
深层展开的网络（DUN）已被证明是一种可行的压缩感（CS）。在这项工作中，我们提出了一个用于自然图像CS的DUN，称为低级CS网络（LR-CSNET）。现实世界的图像贴片通常通过低级别近似值来代表。 LR-CSNET通过在CS优化任务之前添加低级来利用此属性。我们使用变量拆分得出相应的迭代优化过程，然后将其转换为新的DUN体系结构。该体系结构使用低级生成模块（LRGMS），这些模块学习低级别的基质因子化以及梯度下降和近端映射（GDPMS），提议提取高频功能以完善图像详细信息。此外，在DUN的每个重建阶段生成的深度特征都会在阶段之间转移，以提高性能。我们在三个广泛考虑的数据集上进行的广泛实验证明了与自然图像CS中最新方法相比，LR-CSNET的表现有希望。
Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS. Real-world image patches are often well-represented by low-rank approximations. LR-CSNet exploits this property by adding a low-rank prior to the CS optimization task. We derive a corresponding iterative optimization procedure using variable splitting, which is then translated to a new DUN architecture. The architecture uses low-rank generation modules (LRGMs), which learn low-rank matrix factorizations, as well as gradient descent and proximal mappings (GDPMs), which are proposed to extract high-frequency features to refine image details. In addition, the deep features generated at each reconstruction stage in the DUN are transferred between stages to boost the performance. Our extensive experiments on three widely considered datasets demonstrate the promising performance of LR-CSNet compared to state-of-the-art methods in natural image CS.
