Open AccessConstruction model for total variation regularization parameter
Author(s) -
G. Gong,
Hongming Zhang,
Minyu Yao
Publication year - 2014
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.22.010500
Subject(s) - regularization (linguistics) , total variation denoising , deconvolution , computer science , noise reduction , image quality , blind deconvolution , algorithm , image restoration , image processing , optics , mathematical optimization , mathematics , image (mathematics) , artificial intelligence , physics
Image denoising is important for high-quality imaging in adaptive optics. Richardson-Lucy deconvolution with total variation(TV) regularization is commonly used in image denoising. The selection of TV regularization parameter is an essential issue, yet no systematic approach has been proposed. A construction model for TV regularization parameter is proposed in this paper. It consists of four fundamental elements, the properties of which are analyzed in details. The proposed model bears generality, making it apply to different image recovery scenarios. It can achieve effective spatially adaptive image recovery, which is reflected in both noise suppression and edge preservation. Simulations are provided as validation of recovery and demonstration of convergence speed and relative mean-square error.