An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal

The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been proposed in recent years. In contrast to these existing models, in this paper, we propose a nonconvex model with a DC functio...

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Bibliographic Details
Published in:Numerical algorithms Vol. 97; no. 3; pp. 1119 - 1139
Main Authors: Wang, Chengjing, Zhao, Xile, Wang, Qingsong, Ma, Zepei, Tang, Peipei
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2024
Springer Nature B.V
Subjects:
Algebra
Algorithms
Computer Science
Data analysis
Image contrast
Image degradation
Image quality
Noise
Numeric Computing
Numerical Analysis
Optimization
Original Paper
Regularization methods
Remote sensing
Theory of Computation
90C20
Group sparsity
Alternating direction method of multipliers
65K05
90C25
90C06
49M27
Remote sensing
Proximal majorization-minimization algorithm
ISSN:1017-1398, 1572-9265
Online Access:Get full text
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Summary:The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been proposed in recent years. In contrast to these existing models, in this paper, we propose a nonconvex model with a DC function (i.e., the difference of convex functions) structure to remove the strip noise. To solve this model, we make use of the DC structure and apply an inexact proximal majorization-minimization algorithm with each inner subproblem solved by the alternating direction method of multipliers. It deserves mentioning that we design an implementable stopping criterion for the inner subproblem, while the convergence can still be guaranteed. Numerical experiments demonstrate the superiority of the proposed model and algorithm.
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ISSN:1017-1398
1572-9265
DOI:10.1007/s11075-023-01743-2