Solving total-variation image super-resolution problems via proximal symmetric alternating direction methods

Journal of Inequalities and Applications

卷:2016

Shiming Xu¹  Bin Gao¹  Fenggang Sun²  Ying Tong³ 

[1] College of Communications Engineering, PLA University of Science and Technology;

[2] College of Information Science and Engineering, Shandong Agricultural University;

[3] Department of Communication Engineering, Nanjing Institute of Technology;

关键词: proximal symmetric alternating direction method of multipliers;    linearized Peaceman-Rechford splitting method;    convex minimization;    strictly contractive;    single image super-resolution;   

DOI  :  10.1186/s13660-016-1136-7

来源: DOAJ

【 摘 要 】

Abstract The single image super-resolution (SISR) problem represents a class of efficient models appealing in many computer vision applications. In this paper, we focus on designing a proximal symmetric alternating direction method of multipliers (SADMM) for the SISR problem. By taking full exploitation of the special structure, the method enjoys the advantage of being easily implementable by linearizing the quadratic term of subproblems in the SISR problem. With this linearization, the resulting subproblems easily achieve closed-form solutions. A global convergence result is established for the proposed method. Preliminary numerical results demonstrate that the proposed method is efficient and the computing time is saved by nearly 40% compared with several state-of-the-art methods.

【 授权许可】

Unknown