Image Stitching Based on Image Grayscale

Image stitching technology based on grayscale analysis enables the combination of two partially overlapping images into a wider-field composite image. This technique finds applications in various domains such as geographic mapping, photography, and virtual reality systems. By implementing image stitching in these fields, users can obtain more comprehensive and detailed visual information, delivering enhanced visual experiences. The core implementation typically involves grayscale-based feature detection algorithms (e.g., Harris corner detection or SIFT) to identify keypoints, followed by similarity calculation using normalized cross-correlation or phase correlation methods. The transformation matrix (often estimated through RANSAC algorithm for robustness) then aligns the images seamlessly. Critical functions include feature matching validation and blending operations (like multi-band blending) to ensure smooth transitions and consistent visual quality in the stitched result. Consequently, grayscale-based image stitching holds significant importance in modern image processing and computer vision applications.