Robust Alignment for Panoramic Stitching via an Exact Rank Constraint
This addresses the challenge of robust panoramic stitching for applications like photography and computer vision, offering a novel method for global error handling.
The paper tackles the problem of image alignment for panoramic stitching by proposing a pixel-based algorithm that formulates alignment as rank-1 and sparse matrix decomposition, achieving more accurate alignment and higher quality stitched images than state-of-the-art methods.
We study the problem of image alignment for panoramic stitching. Unlike most existing approaches that are feature-based, our algorithm works on pixels directly, and accounts for errors across the whole images globally. Technically, we formulate the alignment problem as rank-1 and sparse matrix decomposition over transformed images, and develop an efficient algorithm for solving this challenging non-convex optimization problem. The algorithm reduces to solving a sequence of subproblems, where we analytically establish exact recovery conditions, convergence and optimality, together with convergence rate and complexity. We generalize it to simultaneously align multiple images and recover multiple homographies, extending its application scope towards vast majority of practical scenarios. Experimental results demonstrate that the proposed algorithm is capable of more accurately aligning the images and generating higher quality stitched images than state-of-the-art methods.