| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0546240 |
| utime |
20250123085732.2 |
| mtime |
20211005235959.9 |
| SCOPUS |
85121685297 |
| WOS |
000819455102016 |
| DOI |
10.1109/ICIP42928.2021.9506502 |
| title
(primary) (eng) |
Improving Neural Blind Deconvolution |
| specification |
| page_count |
5 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0546361 |
| ISBN |
978-1-6654-4115-5 |
| ISSN |
2381-8549 |
| title
|
2021 IEEE International Conference on Image Processing : Proceedings |
| page_num |
1954-1958 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2021 |
|
|
| keyword |
blind deblurring |
| keyword |
SelfDeblur |
| keyword |
deep image prior |
| author
(primary) |
| ARLID |
cav_un_auth*0293863 |
| name1 |
Kotera |
| name2 |
Jan |
| 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*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| 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 |
|
| cas_special |
| project |
| project_id |
GA20-27939S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0391986 |
|
| abstract
(eng) |
The field of blind image deblurring was for a long time dominated by Maximum-A-Posteriori methods seeking the optimal pair of sharp image--blur of a suitable functional. Recently, learning-based methods, especially those based on deep convolutional neural networks, are proving effective and are receiving increasing attention by the research community. In 2020, Ren~et~al. proposed a deblurring method called SelfDeblur which combines the model-driven approach of traditional MAP methods and the generative power of neural nets. The method is capable of producing very high-quality results, yet it inherits some problems of MAP methods, especially possible convergence to a wrong local optimum. In this paper we propose several easy-to-implement modifications of SelfDeblur, namely suitable initialization, multiscale processing, and regularization, that improve the average performance of the original method and decrease the probability of failure. |
| action |
| ARLID |
cav_un_auth*0414773 |
| name |
IEEE International Conference on Image Processing (ICIP) 2021 |
| dates |
20210919 |
| mrcbC20-s |
20210922 |
| place |
Anchorage |
| country |
US |
|
| RIV |
JD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20204 |
| reportyear |
2022 |
| num_of_auth |
3 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0322888 |
| confidential |
S |
| arlyear |
2021 |
| mrcbU14 |
85121685297 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000819455102016 WOS |
| mrcbU63 |
cav_un_epca*0546361 2021 IEEE International Conference on Image Processing : Proceedings IEEE 2021 Piscataway 1954 1958 978-1-6654-4115-5 2381-8549 |
|