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

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
Main Authors: Daihong Jiang, Zhixiang Chen, Sanyou Zhang, Yunfei Li, Lu Zhao
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Low rank decomposition
defect detection
function penalty
operator splitting algorithm
Online Access:https://ieeexplore.ieee.org/document/11119630/
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https://ieeexplore.ieee.org/document/11119630/

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