Work

Sparse Adaptive Optimization Based on Low Rank Decomposition for Image Defect Detection

Year: 2025

Type: article

Abstract: Low-rank optimization plays a pivotal role in image processing due to its inherent ability to capture low-dimensional structures and promote sparsity. Traditional low-rank decomposition methods aim to recover low-rank components and isolate sparse elements, but the structural integrity of the sparse components is often compromised. Moreover, the interplay between low-rank and sparse terms can lead to conflicting effects in practical scenarios. To address these limitations, this paper proposes a novel low-rank decomposition model that introduces a new form of regularization, effectively balancing low-rank representation and structured sparsity. The proposed model enhances the structural expressiveness of the sparse component while preserving the global low-rank structure. To solve the resulting optimization problem efficiently, we develop a tailored algorithm based on operator splitting techniques, significantly improving computational efficiency without sacrificing accuracy. Extensive experiments on tasks such as defect detection and background reconstruction demonstrate that the proposed method outperforms existing approaches, achieving superior accuracy and robustness across various real-world scenarios.

Source: IEEE Access

DOI: 10.1109/access.2025.3596642

Authors: Daihong Jiang, Zhixiang Chen, Sanyou Zhang, Yunfei Li, Lu Zhao

Institutions: Xuzhou University of Technology, Central South University, China University of Mining and Technology

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Science and Technology Plan Project of Taizhou