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

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...

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Veröffentlicht in:IEEE access Jg. 13; S. 139433 - 139444
Hauptverfasser: Jiang, Daihong, Chen, Zhixiang, Zhang, Sanyou, Li, Yunfei, Zhao, Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Computational modeling
Decomposition
Defect detection
Defects
Fabrics
Feature extraction
function penalty
Image edge detection
Image processing
Image reconstruction
Low rank decomposition
Matrix decomposition
Noise
operator splitting algorithm
Optimization
Regularization
Sparse matrices
Sparsity
Structural integrity
ISSN:2169-3536, 2169-3536
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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3596642