Image enhancement method for paint defects based on polarization fusion technique

The highly reflective nature of paint surfaces poses a challenge for defect detection. In this paper, a polarization fusion based image enhancement algorithm for paint defects is proposed to improve the image quality and complement the masked defect details. Firstly, the linearly polarized photometric (DOLP) image is initially fused with the angle of polarization (AOP) image. The light intensity images and polarization feature images were enhanced using a single scale Retinex algorithm and these images were decomposed and reconstructed using wavelet transform to integrate the low and high frequency subbands based on saliency maps and regional energy features. Experiments were performed on defects at different locations and compared with other classical methods. The results show that the proposed fusion algorithm improves the contrast while providing more complete defect information, clearer texture details and significant advantages over other algorithms.