Papoutsellis, E; Kereta, Z; Papafitsoros, K; (2025) Why do we Regularise in Every Iteration for Imaging Inverse Problems? In: Bubba, Tatiana A and Gaburro, Romina and Gazzola, Silvia and Papafitsoros, Kostas and Pereyra, Marcelo and Schönlieb, Carola-Bibiane, (eds.) Scale Space and Variational Methods in Computer Vision. SSVM 2025. (pp. pp. 43-55). Springer Nature

Text 2411.00688v1.pdf - Accepted Version Access restricted to UCL open access staff until 18 May 2026. Download (3MB)

Abstract

Regularisation is a common method in iterative solutions for imaging inverse problems. The majority of algorithms evaluate the proximal operator of the regulariser in every iteration, leading to a significant computational overhead, as such evaluations can be costly. In this context, we investigate skipping the regulariser to reduce the frequency of proximal operator computations. This approach shows a reduction in computational time without compromising convergence or image quality. Here we study for the first time the efficacy of regularisation skipping on a variety of imaging inverse problems. We build upon the ProxSkip algorithm and we also propose a novel skip-version of the PDHG algorithm. Extensive numerical results highlight the potential of these methods to accelerate computations while maintaining high-quality reconstructions.

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