Open AccessRobust, blind multichannel image identification and restoration using stack decoder
Author(s) -
Boudjenouia Fouad,
AbedMeraim Karim,
Chetouani Aladine,
Jennane Rachid
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5243
Subject(s) - robustness (evolution) , computer science , image restoration , deconvolution , blind deconvolution , algorithm , decoding methods , channel (broadcasting) , identification (biology) , mathematical optimization , norm (philosophy) , image (mathematics) , artificial intelligence , image processing , mathematics , telecommunications , biochemistry , chemistry , botany , biology , political science , law , gene
In this study, the authors introduce new solutions and improvements to the multi‐channel blind image deconvolution problem. More precisely, authors’ contributions are threefold: (i) At first, a simplified version of the existing cross‐relation method for blind system identification is proposed; but most importantly, the authors incorporate into the channel estimation cost function a sparsity constraint to deal with the challenging issue of channel order overestimation errors; (ii) then, once the channel identification is achieved, a new image restoration method based on the stack decoding algorithm is introduced; and (iii) finally, a refining approach using an ‘all‐at‐once’ optimisation technique with an improved mixed norm regularisation is considered. The performance of the proposed approach was evaluated using several numerical simulations. Blind system identification and image restoration tasks were evaluated with respect to several criteria: numerical complexity, robustness to noise effects and channel order estimation. The results obtained are promising and highlight the effectiveness of the proposed approach.