Image Stitching

Image stitching combines multiple images to create a single, wider-field-of-view image, addressing limitations of individual camera perspectives. Current research emphasizes robust methods for handling challenges like parallax (depth discrepancies), improving alignment accuracy using techniques such as multi-homography warping and epipolar geometry, and enhancing the visual quality of stitched images through advanced blending and inpainting methods, often incorporating deep learning models like UNet and diffusion models. These advancements are significant for applications ranging from medical imaging (e.g., endoscopy, X-ray) and augmented reality to panoramic photography and robotics, enabling more comprehensive scene understanding and improved data analysis.