Open AccessDistributed perceptual compressed sensing framework for multiview images
Author(s) -
Elsayed S.,
Elsabrouty M.,
Muta O.,
Furukawa H.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.0477
Subject(s) - computer science , artificial intelligence , computer vision , compressed sensing , residual , joint (building) , compensation (psychology) , signal (programming language) , image restoration , perception , pattern recognition (psychology) , image (mathematics) , image processing , algorithm , engineering , architectural engineering , psychology , neuroscience , psychoanalysis , biology , programming language
A perceptual‐based compressed sensing (CS), which focuses the measurements and the recovery on the visually important low‐frequency coefficients, is applied for multi‐view image signals. High correlation among different views is exploited to generate signal prediction using disparity estimation and compensation techniques. A residual‐based recovery is utilised as a joint recovery for the non‐reference images to enhance the reconstruction performance. The proposed framework shows remarkable performance improvement over the conventional CS with joint recovery as well as the perceptual‐based CS with independent recovery.