SOURCE-LINKED INTELLIGENCE
Newton Deep Unfolding for Compressed Sensing
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 th
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- arXiv · AI, language, vision and robotics · 2026-09-13T08:56:59.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.