Open AccessFast proximal splitting algorithm for constrained TGV‐regularised image restoration and reconstruction
Author(s) -
He Chuan,
Hu Changhua,
Qi Naixin,
Zhu Xiaofei,
Liu Lianxiong
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.2018.5078
Subject(s) - image restoration , algorithm , image (mathematics) , computer science , iterative reconstruction , mathematics , artificial intelligence , mathematical optimization , image processing
A fast algorithm is proposed to tackle the constrained total generalised variation (TGV)‐based image‐restoration and reconstruction problems. The proposed algorithm proceeds by splitting: the non‐smooth constrained TGV model is first decomposed into several sub‐problems easier to solve, and then the linear gradient or proximity operators, including projections and shrinkages, of the sub‐problems are individually called without inner iteration. The algorithm is highly parallel since most of its steps can be executed simultaneously. Image‐restoration and reconstruction experiments demonstrate that the proposed algorithm outperforms several state‐of‐the‐art TV‐based methods both in accuracy and high‐speed efficiency. Besides, the proposed method efficiently suppresses staircase effects and presents better visual impression.