压缩感知的牛顿深度展开
Newton Deep Unfolding for Compressed Sensing
- School of Mechatronic Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出NDU-Net，首个利用二阶优化（牛顿法）的深度展开网络，通过牛顿更新模块和多尺度先验模块，在压缩感知重建中取得优异性能与鲁棒性。
AI中文摘要：
压缩感知（CS）从高度受限的测量中重建图像，但现有的深度展开方法通常由一阶优化驱动，并且对重建过程中生成的优化状态利用不足。为了解决这些局限性，我们提出了一种牛顿深度展开网络（NDU-Net），据我们所知，这是首个利用二阶优化进行CS重建的深度展开框架。具体而言，NDU-Net引入了一个牛顿更新（NU）模块来估计牛顿型更新方向，并生成表征当前重建过程的优化状态。此外，设计了一个牛顿引导的多尺度先验（MP）模块，将这些优化状态融入多尺度特征恢复中，从而使学习到的先验能够适应当前的重建阶段。在不同CS比率下的实验结果表明，我们提出的NDU-Net实现了有前景的重建性能，并表现出增强的鲁棒性。我们的代码可在以下网址获取：https://this URL。
英文摘要：
Compressed sensing (CS) reconstructs images from highly limited measurements, but existing deep unfolding methods are typically driven by first-order optimization and weakly exploit the optimization states generated during reconstruction. To address these limitations, we propose a Newton deep unfolding network (NDU-Net), which, to the best of our knowledge, is the first deep unfolding framework that leverages second-order optimization for CS reconstruction. Specifically, NDU-Net introduces a Newton update (NU) module to estimate Newton-type update directions and generate optimization states that characterize the current reconstruction process. Furthermore, a Newton-guided multi-scale prior (MP) module is designed to incorporate these optimization states into multi-scale feature restoration, thereby enabling the learned prior to adapt to the current reconstruction stage. Experimental results under different CS ratios confirm that our proposed NDU-Net achieves promising reconstruction performance and exhibits enhanced robustness. Our code is available at https://github.com/xianchaoxiu/DNU-Net.
