0638576 20250918073959.420250903235959.9 10.1007/978-3-031-87213-6_14 7 s. P cav_un_epca*0638911978-3-031-87212-9107-113ChamSpringer2025HasanoğluA. H.NovikovR.Van BockstalK. Inverse problems Image restoration Deep learning Regularization cav_un_auth*0379363 Kerepecký Tomáš UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101209 Šroubek Filip UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2025/ZOI/kerepecky-0638576.pdf https://link.springer.com/book/10.1007/978-3-031-87213-6 GA21-03921S GA ČR cav_un_auth*0412209 This work addresses inverse problems in image restoration, focusing on recovering high-quality images from degraded observations, a critical task in fields like microscopy and digital photography. We examine both traditional variational methods and modern deep learning techniques, highlighting hybrid approaches that merge mathematical modeling with data-driven learning. Classical model-based methods use explicit regularization, like total variation, to incorporate prior knowledge and stabilize the inversion process. Meanwhile, deep learning approaches, both supervised and self-supervised, leverage implicit regularization, where network architectures capture and learn prior information from data. We present our recent advancements in this field and discuss the effectiveness of these complementary approaches in solving complex image restoration problems in theory and practice. cav_un_auth*0493164 Inverse Problems: Modelling and Simulation 2024 (IPMS 2024) 20240526 20240601 Paradise Bay Resort Hotel MT IN 10000 10200 10201 2026 PR RVO:67985556 https://hdl.handle.net/11104/0369466 S 2025 SCOPUS PUBMED WOS cav_un_epca*0638911 Inverse Problems: Modelling and Simulation : Extended Abstracts of the IPMS Conference 2024 Springer 2025 Cham 107 113 978-3-031-87212-9 Hasanoğlu A. H. 340 Novikov R. 340 Van Bockstal K. 340