| bibtype |
J -
Journal Article
|
| ARLID |
0490175 |
| utime |
20240103220111.5 |
| mtime |
20180610235959.9 |
| SCOPUS |
85047017461 |
| WOS |
000434450200004 |
| DOI |
10.1109/LSP.2018.2836964 |
| title
(primary) (eng) |
An Adaptive Correlated Image Prior for Image Restoration Problems |
| specification |
| page_count |
5 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0253212 |
| ISSN |
1070-9908 |
| title
|
IEEE Signal Processing Letters |
| volume_id |
25 |
| volume |
7 (2018) |
| page_num |
1024-1028 |
| publisher |
| name |
Institute of Electrical and Electronics Engineers |
|
|
| keyword |
adaptive image prior |
| keyword |
image restoration |
| keyword |
variational Bayes |
| author
(primary) |
| ARLID |
cav_un_auth*0019204 |
| name1 |
Ševčík |
| name2 |
J. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| 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 |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| institution |
UTIA-B |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0353427 |
| project_id |
LO1607 |
| agency |
GA MŠk |
| country |
CZ |
|
| project |
| ARLID |
cav_un_auth*0361425 |
| project_id |
GA18-05360S |
| agency |
GA ČR |
|
| abstract
(eng) |
Image restoration is typically defined as an ill-posed problem which has to be regularized to obtain an acceptable solution. In Bayesian interpretation, regularization is equivalent to prior model of the image. An added value of Bayesian point of view is the ability to form a hierarchical model and estimate the hyper-parameters of the prior from the data. Many prior models are available, usually based on automatic relevance determination principle applied to the transformed image. However, the transformation (the most common is a differential operator) is assumed to be known. In this paper, we propose to relax this assumption and estimate the image transformation from the data. The resulting algorithm is analytically tractable using the Variational Bayes method. Properties of the new prior are demonstrated on the problem of image super-resolution.\n |
| result_subspec |
WOS |
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2019 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 20231122143229.6 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0284543 |
| mrcbC64 |
1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS |
| confidential |
S |
| mrcbC86 |
3+4 Article Engineering Electrical Electronic |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.037 |
| mrcbT16-s |
0.785 |
| mrcbT16-B |
75.59 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q2 |
| arlyear |
2018 |
| mrcbTft |
\nSoubory v repozitáři: smidl-0490175.pdf |
| mrcbU14 |
85047017461 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000434450200004 WOS |
| mrcbU63 |
cav_un_epca*0253212 IEEE Signal Processing Letters 1070-9908 1558-2361 Roč. 25 č. 7 2018 1024 1028 Institute of Electrical and Electronics Engineers |
|