TY - JOUR
T1 - Structural Similarity-Based Nonlocal Variational Models for Image Restoration
AU - Wang, Wei
AU - Li, Fang
AU - Ng, Michael K.
N1 - Funding Information:
Manuscript received April 4, 2018; revised September 30, 2018 and January 14, 2019; accepted March 5, 2019. Date of publication March 20, 2019; date of current version July 1, 2019. The work of W. Wang was supported in part by the Natural Science Foundation of Shanghai and in part by the Fundamental Research Funds for the Central Universities of China under Grant 22120180255 and Grant 22120180067. The work of F. Li was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 11671002 and Grant 61731009 and in part by the Science and Technology Commission of Shanghai Municipality under Grant 18dz2271000. The work of M. K. Ng was supported in part by the HKRGC GRF under Grant 1202715, Grant 12306616, Grant 12200317, and Grant 12300218, and in part by HKBU under Grant RC-ICRS/16-17/03. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Damon M. Chandler. (Corresponding author: Wei Wang.) W. Wang is with the School of Mathematical Sciences, Tongji University, Shanghai 200092, China (e-mail: [email protected]).
PY - 2019/9
Y1 - 2019/9
N2 - In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models.
AB - In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models.
KW - gradient
KW - Image restoration
KW - nonlocal variational model
KW - regularization
KW - structural similarity index
UR - http://www.scopus.com/inward/record.url?scp=85068451104&partnerID=8YFLogxK
U2 - 10.1109/TIP.2019.2906491
DO - 10.1109/TIP.2019.2906491
M3 - Journal article
C2 - 30908219
AN - SCOPUS:85068451104
SN - 1057-7149
VL - 28
SP - 4260
EP - 4272
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 9
M1 - 8672180
ER -