Image restoration by using a modified proximal point algorithm-Reference-Cited by-同舟云学术

Image restoration by using a modified proximal point algorithm

Published:2023 Issue:4 Volume:8 Page:9557-9575
ISSN:2473-6988
Container-title:AIMS Mathematics
language:
Short-container-title:MATH

Author:

Arunchai Areerat¹,Seangwattana Thidaporn²³,Sitthithakerngkiet Kanokwan⁴³,Sombut Kamonrat⁵³

Affiliation:

1. Department of Mathematics and Statistics, Faculty of Science and Technology, Nakhon Sawan Rajabhat University, 60000, Nakhon Sawan, Thailand

2. Faculty of Science Energy and Environment, King Mongkut's University of Technology North Bangkok, Rayong Campus (KMUTNB), 21120, Rayong, Thailand

3. Applied Mathematics for Science and Engineering Research Unit (AMSERU), Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi (RMUTT), 12110, Rungsit-Nakorn Nayok Rd., Klong 6, Khlong Luang, Thanyaburi, Pathum thani, Thailand

4. Intelligent and Nonlinear Dynamic Innovations Research Center, Department of Mathematics Faculty of Applied Science, King Mongkut's University of Technology North Bangkok (KMUTNB), 10800, Bangkok, Thailand

5. Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi (RMUTT), 12110, Pathum Thani, Thailand

Abstract

<abstract>In this paper, we establish a modified proximal point algorithm for solving the common problem between convex constrained minimization and modified variational inclusion problems. The proposed algorithm base on the proximal point algorithm in [<xref ref-type="bibr" rid="b19">19</xref>] and the method of Khuangsatung and Kangtunyakarn in [<xref ref-type="bibr" rid="b21">21</xref>] by using suitable conditions in Hilbert spaces. The proposed algorithm is not only presented in this article; however has also been demonstrated to generate a robust convergence theorem. The proposed algorithm could be used to solve image restoration problems where the images have suffered a variety of blurring operations. Additionally, we contrast the signal-to-noise ratio (SNR) of the proposed algorithm against that of Khuangsatung and Kangtunyakarn's method in [<xref ref-type="bibr" rid="b21">21</xref>] in order to compare image quality.</abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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