Open AccessOmni‐gradient‐based total variation minimisation for sparse reconstruction of omni‐directional image
Author(s) -
Lou Jingtao,
Li Yongle,
Liu Yu,
Tan Shuren,
Zhang Maojun
Publication year - 2014
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.2013.0330
Subject(s) - minimisation (clinical trials) , catadioptric system , computer science , computer vision , compressed sensing , artificial intelligence , iterative reconstruction , image restoration , image quality , image (mathematics) , image processing , mathematics , optics , statistics , physics , lens (geology)
Total variation (TV) minimisation algorithms have been successfully applied in compressive sensing (CS) recovery for natural images owing to its advantage of preserving edges. However, traditional TV is no longer appropriate for omni‐directional image processing because of the distortions in catadioptric imaging systems. The omni‐gradient computing method combined with the characteristics of omni‐directional imaging is proposed in this study. To reconstruct the image from its compressive samples, the omni‐total variation (omni‐TV) regularisation based on omni‐gradient is utilised instead of traditional TV during the image restoration. The experimental results show that the omni‐directional images can be reconstructed effectively and accurately. Compared with the classical TV minimisation model, the images recovered based on omni‐TV model can provide higher quality both in subjective evaluation and objective evaluation.