Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction

Surface defect inspection of TFT-LCD panels is a critical task in LCD manufacturing. In this paper, an automatic defect inspection method based on the low-rank matrix reconstruction is proposed. The textured background of the LCD image is a low-rank matrix and the foreground image with defects can b...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2015-02, Vol.149, p.1206-1215
Main Authors: Cen, Yi-Gang, Zhao, Rui-Zhen, Cen, Li-Hui, Cui, Li-Hong, Miao, Zhen-Jiang, Wei, Zhe
Format: Article
Language:English
Subjects:
Adaptive parameter selection
Algorithms
Defect inspection
Defects
IALM
Image reconstruction
Inspection
LCDs
Liquid crystal displays
Low-rank matrix reconstruction
Panels
Reconstruction
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Summary:Surface defect inspection of TFT-LCD panels is a critical task in LCD manufacturing. In this paper, an automatic defect inspection method based on the low-rank matrix reconstruction is proposed. The textured background of the LCD image is a low-rank matrix and the foreground image with defects can be treated as a sparse matrix. By utilizing the Inexact Augmented Lagrange Multipliers (IALM) algorithm, the segmentation of a LCD image can be converted into the reconstruction of a low-rank matrix with a fraction of its entries arbitrarily corrupted. This low-rank matrix reconstruction problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l1-norm. Also, adaptive parameter selection strategy is proposed by conducting deep analysis on the IALM algorithm, which improves the generality of the IALM algorithm for different defect types. Experiment results show that our inspection algorithm is robust for the defect shapes and types under different illumination conditions. The shapes and edges of defect areas in the LCD images can be well preserved and segmented from textured background by our detection algorithm.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.09.007