首页|Proximal Linearized Minimization Algorithm for Nonsmooth Nonconvex Minimization Problems in Image Deblurring with Impulse Noise
Proximal Linearized Minimization Algorithm for Nonsmooth Nonconvex Minimization Problems in Image Deblurring with Impulse Noise
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Impulse noise removal is an important task in image restoration.In this paper,we introduce a general nonsmooth nonconvex model for recovering images degraded by blur and impulsive noise,which can easily include some prior information,such as box constraint or low rank,etc.To deal with the nonconvex problem,we employ the proximal linearized minimization algorithm.For the subproblem,we use the alternating direction method of multipliers to solve it.Furthermore,based on the assumption that the objective function satisfies the Kurdyka-Lojasiewicz property,we prove the global convergence of the proposed algorithm.Numerical experiments demonstrate that our method outperforms both the l1TV and Nonconvex TV mod-els in terms of subjective and objective quality measurements.
nonconvex data fidelity termimpulse noisetotal variationproximal linearized minimization
Shirong DENG、Yuchao TANG
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School of Mathematics and Information Science,Guangzhou University,Guangdong 510006,P.R.China
Department of Mathematics,Nanchang University,Jiangxi 330031,P.R.China
国家自然科学基金国家自然科学基金Guangzhou Education Scientific Research Project 2024江西省自然科学基金
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