An image fusion-based method for recovering the 3D shape of roll surface defects

Most of the existing studies on roll surface defects focus on qualitative detection and lack quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are the stylus method, laser scanning method, and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called modified multi-state pulse-coupled neural network to obtain a fully focused image. The method achieves 3D shape recovery of defects by modeling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. This method significantly improves the quality of defect detection images, with a 98% better gradient and a 28% increase in overall image quality. Additionally, it keeps 3D reconstruction errors under 4%, ensuring high accuracy and noise resistance.