Leveraging Local Patch Alignment to Seam-cutting for Large Parallax Image Stitching

Abstract: Seam cutting methods have been proven effective in the composition step of image stitching, especially for images with parallax. However, current seam cutting can be seen as the subsequent step after the image alignment is settled. Its effectiveness usually depends on the fact that images can be roughly aligned such that a local region exists where an unnoticeable seam can be found. Current alignment methods often fall short of expectations for images with large parallax, and most efforts are devoted to improving the alignment accuracy. In this paper, we argue that by adding a simple Local Patch Alignment Module (LPAM) into the seam cutting, the final result can be efficiently improved for large parallax image stitching. Concretely, we first evaluate the quality of pixels along the estimated seam of the seam cutting method. Then, for pixels with low qualities, we separate their enclosing patches in the aligned images and locally align them by constructing modified dense correspondences via SIFT flow. Finally, we composite the aligned patches via seam cutting and merge them into the original aligned result to generate the final mosaic. Experiments show that introducing LPAM can effectively and efficiently improve the stitching results.