Year: 2024
Type: article
Abstract: In this study, we introduce a novel self-adaptive alternating direction method of multipliers tailored for image denoising. Our approach begins by formulating a collaborative regularization model that upholds structured sparsity within images while delving into spatial correlations among pixels. To address the challenge of penalty parameter influence on convergence speed, we innovate by proposing a self-adaptive alternating direction method of multipliers. This adaptive technique autonomously adjusts variable penalty parameters to expedite algorithm convergence, thereby markedly boosting algorithmic performance. Through a fusion of simulations and empirical analyses, our research demonstrates that this novel methodology significantly amplifies the efficacy of denoising processes.
Source: Applied Sciences
DOI: 10.3390/app142210427
Authors: Mingjie Xie, Haibing Guo
Institution: Jiangsu Ocean University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4404326097