Fabric Defect Detection Based on Sparse Representation Image Decomposition.

Due to the distribution of fabric defect shown the sparseness, it is possible to describe the fabric defects feature using sparse representation in particular transform. In this paper, we proposed a novel approach based on sparse representation for detecting patterned fabric defect. In our work, the defective fabric image is expressed by sparse representation model, it is represented as a linear superposition of three components: defect, background and noise. The defective components can be decomposed effectively by using the principle of base pursuit denoising algorithm and block coordination relaxation algorithm. The fabric defect detection is realized by analyzing the defect components. Experimental results demonstrate that the proposed approach is more efficient to detect a variety of fabric defects, in particularly the pattern fabrics.