Non-uniform and low illumination image enhancement for cabinet surface defect detection

Illumination plays an important role in the surface defect detection of large cabinet. The quality of cabinet surface image captured in uneven or low illumination condition is poor, which may lead to defect detection error. To solve this problem, an image enhancement method is proposed by combining cartoon texture decomposition and optimal hyperbolic tangent curve algorithm. Firstly, cartoon and texture maps are separated from cabinet images using an orientation filter. The image illumination model is also formulated based on the Gaussian scale space theory, and the uneven illumination is removed. Secondly, the hyperbolic tangent curve is used to enhance the low-illumination image by the weighted stretching. Finally, the performance of the proposed image enhancement method is evaluated using the contrast, brightness and gray-scale variance product parameters. The method performance is also evaluated based on the comparison results of defect detection on the original captured image and the enhanced images. Experimental results show that the proposed method is suitable to enhance the cabinet image captured under the uneven and low illumination condition. The accuracy of defect detection on enhanced images is significantly improved. To be specific, the recall ratio is increased by 29% and the F-measure value is increased by 21%. © 2019, Science Press. All right reserved.