Abstract:

A novel fabric defect detection algorighm based on sparse optimization is proposed. Firstly, an adaptive dictionary is learned from test fabric image using L1-norm minimization method, the test fabric image is sparsely represented using the learned dictionary, and then the coefficient matrix of sparse representation is xalculated. Secondly, the abnormal coefficients are removed using optimization function, then a new image is reconstructed using the optimized coefficient matrix and the dictionary. Finally, the reconstructed image is subtracted from original test image to acquire a residual image, and then the maximum entropy threshold method is used to segment the defect region. Experimental results demonstrate that the proposed algorithm has higher detection accuracy comparing with the state of the art.

Key words: L1-norm, sparse representation, textile image, defect detection