|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM35520181X |
| 003 |
DE-627 |
| 005 |
20231226063839.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2022.3224322
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1183.xml
|
| 035 |
|
|
|a (DE-627)NLM35520181X
|
| 035 |
|
|
|a (NLM)37015435
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Huang, Yunshi
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 04.04.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel, integrating the VBA within a neural network paradigm following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and leads to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Chouzenoux, Emilie
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Pesquet, Jean-Christophe
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2022) vom: 20. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2022
|g day:20
|g month:12
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2022.3224322
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d PP
|j 2022
|b 20
|c 12
|