NDLI: Fast proximal splitting algorithm for constrained TGV-regularised image restoration and reconstruction

Content Provider	IET Digital Library
Author	He, Chuan Hu, Changhua Qi, Naixin Zhu, Xiaofei Liu, Lianxiong
Abstract	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.
Starting Page	576
Ending Page	582
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 4, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5078
Journal	IET Image Processing
Publisher Date	2018-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algorithm Proceeds Computer Vision And Image Processing Technique Constrained TGV-regularised Image Restoration Constrained Total Generalised Variation-based Image-restoration Fast Algorithm Fast Proximal Splitting Algorithm Image Denoising Image Reconstruction Image Restoration Interpolation And Function Approximation Iterative Method Linear Gradient Nonsmooth Constrained TGV Model Numerical Analysis Optical, Image And Video Signal Processing Optimisation Technique Reconstruction Experiments Reconstruction Problem State-of-the-art TV-based Method Variational Technique
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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