Salehi, M S, Bubba, T A & Korolev, Y 2025, Fast Inexact Bilevel Optimization for Analytical Deep Image Priors. in T A Bubba, R Gaburro, S Gazzola, K Papafitsoros, M Pereyra & C-B Schönlieb (eds), Scale Space and Variational Methods in Computer Vision - 10th International Conference, SSVM 2025, Proceedings. Lecture Notes in Computer Science, vol. 15667 LNCS, Springer, Cham, Switzerland, pp. 30-42, 10th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2025, Dartington, UK United Kingdom, 18/05/25. https://doi.org/10.1007/978-3-031-92366-1_3
The analytical deep image prior (ADP) introduced by Dittmer et al. (2020) establishes a link between deep image priors and classical regularization theory via bilevel optimization. While this is an elegant construction, it involves expensive computations if the lower-level problem is to be solved accurately. To overcome this issue, we propose to use adaptive inexact bilevel optimization to solve ADP problems. We discuss an extension of a recent inexact bilevel method called the method of adaptive inexact descent of Salehi et al.(2024) to an infinite-dimensional setting required by the ADP framework. In our numerical experiments we demonstrate that the computational speed-up achieved by adaptive inexact bilevel optimization allows one to use ADP on larger-scale problems than in the previous literature, e.g. in deblurring of 2D color images.
