跳过部分正则化步骤，加速图像逆问题求解且不降重建质量。
Why do we regularise in every iteration for imaging inverse problems?
- 随机跳过迭代中的正则化步骤，降低计算开销。
- 在多种图像逆问题中保持高质量重建结果。
- 适合需要快速迭代的医学成像与遥感应用。
正则化常用于求解成像逆问题的迭代方法中。许多算法在每一步迭代中都需要计算正则项的近端算子，导致显著的计算开销，因为该计算可能代价高昂。在此背景下，近期为联邦学习提出的ProxSkip算法成为解决方案：它随机跳过正则化步骤，在不损害收敛性的情况下减少迭代算法的计算时间。本文首次探索了ProxSkip在多种成像逆问题中的有效性，并提出了一种新的PDHGSkip版本。大量数值实验表明，这些方法能在保持高质量重建的同时显著加速计算。
原文摘要 · Abstract (English)
Regularisation is commonly used in iterative methods for solving imaging inverse problems. Many algorithms involve the evaluation of the proximal operator of the regularisation term in every iteration, leading to a significant computational overhead since such evaluation can be costly. In this context, the ProxSkip algorithm, recently proposed for federated learning purposes, emerges as an solution. It randomly skips regularisation steps, reducing the computational time of an iterative algorithm without affecting its convergence. Here we explore for the first time the efficacy of ProxSkip to a variety of imaging inverse problems and we also propose a novel PDHGSkip version. Extensive numerical results highlight the potential of these methods to accelerate computations while maintaining high-quality reconstructions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。