Most of the existing studies on roll surface defects focus on qualitative detection and lack of quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are 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 (NSST) is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called Mm-PCNN to obtain a fully focused image. The method achieves 3D shape recovery of defects by modelling 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. The experimental results show that the proposed method effectively integrates detailed information and achieves a reconstruction error control of less than 4%, thus ensuring higher accuracy and improved noise immunity.