Learning edge-preserved image stitching from multi-scale deep homography

Abstract

Image stitching is a classical and challenging technique in computer vision, which aims to generate an image with a wide field of view. The traditional methods heavily depend on feature detection and require the feature points to be dense and evenly distributed in the image, leading to poor robustness in low-texture scenes. Learning methods are rarely studied due to the unavailability of ground truth stitched results, showing unreliable performance on real-world datasets. In this paper, we propose an image stitching learning framework, which consists of a multi-scale deep homography module and an edge-preserved deformation module. First, we design a multi-scale deep homography module to estimate the accurate homography progressively from coarse to fine. After that, an edge-preserved deformation module is designed to learn the deformation rules of image stitching from edge to content, generating the stitched image with artifacts eliminated. Besides, the proposed supervised learning framework can stitch images of arbitrary resolutions and demonstrate good generalization capability in real-world images. Experiments show that our superiority to the existing homography solutions and image stitching algorithms.