Despite advances in surface defect segmentation of steel sheet, it is still far from meeting the needs of real-world applications due to some method usually lack of adaptiveness to different shape, size, location and texture of defect object. Based on the assumption that each defect image is composed of defect-free background components that reflect the similarities of different regions and defect foreground components that reflect unique object information, we formulate the segmentation task as an image decomposition problem. To this end, we develop a double low-rank based matrix factorization framework for decomposing the surface defect image into defect foreground image and defect-free background image. Furthermore, considering the similarity of the defect-free background sub-regions and the defective sub-regions, Laplacian and sparse regularization terms are introduced into the matrix decomposition framework to improve their representation ability and discriminative ability. Importantly, the proposed method is unsupervised and training-free, so it does not requiring a large number of training samples with time-consuming manual labels. Experimental results on synthetic and real-world surface defect images show that the proposed method outperforms some state-of-the-art approaches in terms of both subjective and objective experiments.

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