| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0638576 |
| utime |
20250918073959.4 |
| mtime |
20250903235959.9 |
| DOI |
10.1007/978-3-031-87213-6_14 |
| title
(primary) (eng) |
Inverse Problems in Image Restoration |
| specification |
| page_count |
7 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0638911 |
| ISBN |
978-3-031-87212-9 |
| title
|
Inverse Problems: Modelling and Simulation : Extended Abstracts of the IPMS Conference 2024 |
| page_num |
107-113 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2025 |
|
| editor |
| name1 |
Hasanoğlu |
| name2 |
A. H. |
|
| editor |
|
| editor |
| name1 |
Van Bockstal |
| name2 |
K. |
|
|
| keyword |
Inverse problems |
| keyword |
Image restoration |
| keyword |
Deep learning |
| keyword |
Regularization |
| author
(primary) |
| ARLID |
cav_un_auth*0379363 |
| name1 |
Kerepecký |
| name2 |
Tomáš |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept (eng) |
Department of Image Processing |
| department (cz) |
ZOI |
| department (eng) |
ZOI |
| full_dept |
Department of Image Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101209 |
| name1 |
Šroubek |
| name2 |
Filip |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-03921S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0412209 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0493164 |
| name |
Inverse Problems: Modelling and Simulation 2024 (IPMS 2024) |
| dates |
20240526 |
| mrcbC20-s |
20240601 |
| place |
Paradise Bay Resort Hotel |
| country |
MT |
|
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2026 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0369466 |
| confidential |
S |
| arlyear |
2025 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
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 |
| mrcbU67 |
Hasanoğlu A. H. 340 |
| mrcbU67 |
Novikov R. 340 |
| mrcbU67 |
Van Bockstal K. 340 |
|