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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Bibliographic Details
Published in:	IEEE access Vol. 13; pp. 139433 - 139444
Main Authors:	Jiang, Daihong, Chen, Zhixiang, Zhang, Sanyou, Li, Yunfei, Zhao, Lu
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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