Machine vision image processing technology is an emerging fringe cross-disciplinary discipline developed in the field of image processing in recent years, and the quality inspection of two-dimensional images is an essential link in the printing industry, analyzing the quality defect detection process of two-dimensional images based on machine vision, exploring the relevant factors affecting the accuracy of two-dimensional image quality defect detection based on machine vision, and providing references for the subsequent research and development of automated inspection and quality control of two-dimensional images of printed materials. On this basis, around the gray scale conversion, noise filtering, fixed threshold segmentation, adaptive threshold segmentation, Otsu method and edge detection in image preprocessing, the gray scale statistical information distribution based alignment method and feature based image alignment method in image alignment were summarized, and then the defect extraction and classification of images were summarized and analyzed. The above research content is refined with practical examples. Noise filtering in image preprocessing is used to provide clear images for subsequent defect extraction and reduce artifact interference. Gray scale transformation, threshold segmentation and region of interest extraction in image preprocessing are used to reduce system processing time, laying a solid foundation for efficient defect detection. The image location offset caused by mechanical vibration is eliminated by image registration to ensure the accuracy of subsequent defect extraction. Image defect extraction and classification can help printing companies find production problems and provide targeted improvement measures for the production of high-quality products, thus providing important support.