To address the problems of uneven illumination, low contrast, and excessive surface interference grinding texture in the image of a magnetic tile, this paper proposes a magnetic tile surface defect detection algorithm based on improved homomorphic filtering and Canny algorithm. First, the magnetic tile images were decomposed into high- and low-frequency images using the improved homomorphic filter transfer function. Moreover, the high-frequency images were enhanced through successive mean quantization transform. Second, the enhanced high- and low-frequency images were fused to obtain a magnetic tile image with uniform illumination and high contrast. Furthermore, the Gaussian filter in the traditional Canny algorithm was replaced by relative total variation to extract the magnetic tile defect structure. To improve edge detection accuracy, the gradient direction template was also employed to obtain the gradient amplitude and direction of image pixels, with the single threshold based on image gray information being used to extract the image edge defect, improving the robustness of the algorithm. Finally, the detected image edge defect was filled by morphological processing, and the interference edge was removed to obtain the defect area of the magnetic tile. Experimental results show that the proposed algorithm is effective in detecting the surface defects of magnetic tiles, the detection accuracy is high, and it is suitable for many types of surface defects of magnetic tiles.