Optoelectronic imaging technology is crucial for obtaining environmental information and perceiving the physical world. This technology is widely used in diverse fields, such as aerial remote sensing observation, agricultural resource monitoring, industrial defect detection, and biomedical diagnosis. With the continuous expansion of application scenarios, optoelectronic imaging technology often faces the challenge of balancing a large field of view with high resolution. Therefore, developing accurate and efficient image stitching technology is necessary. This study elucidates the theoretical model of image stitching and classifies and introduces the basic principles and implementation methods of image stitching technology based on regional optimization. This study also analyzes the advantages and limitations of existing regional optimization-based image stitching methods in terms of method characteristics, stitching effect, and time efficiency. By elucidating the application of image stitching technology in typical fields, this paper summarizes the technical challenges and application prospects of image stitching and explores its development in adapting to complex application scenarios, integrating deep learning methods, and improving information dimensions.