Parallax image stitching combining improved feature optimization with optimal seam estimation

Abstract

In the process of image stitching, many problems will inevitably arise, such as misalignment, artifacts and local structure distortion in the overlapping regions. A parallax image stitching algorithm combining improved feature optimization with an innovative iterative seam estimation is proposed. First, the point features and line features of input images are detected. To optimize point features, the histogram statistical approach is proposed to remove false matching points combined with RANSAC algorithm. Second, the mesh warp is optimized by minimizing the sparse quadratic total energy function so as to achieve accurate alignment in the overlapping regions. Finally, we propose an iterative seam estimation method using an improved quality evaluation strategy. Experimental results show that our method has higher matching accuracy and visually better image stitching performance than other methods.