Search

The purpose of image stitching is to obtain a high-definition, seamless panoramic image. Existing methods rely on the accuracy of feature matching, which will misalign images and produce errors such as artifacts and distortions.This paper proposed a new unsupervised real scene image stitching network that can adapt to real scene stitching in the presence of moving targets, ensuring no loss of accuracy in the panorama. It included two networks of alignment and reconstruction, excluding the negative influence of moving targets and misleading regions on the transformation matrix through content branching, and optimizing image details by constraining the reconstruction process through edge branching to achieve high-definition and artifact-free stitching effects. The experimental results show that the method’s RMSE, PSNR, and SSIM reached 1.81, 26.56, and 0.85, respectively. The objective evaluation indexes are better than other classical algorithms overall, and the user research results also indicate that the method obtains higher definition of panoramic images. The method effectively accomplishes unsupervised image stitching in real scenes and can be generalized to stitching tasks in other scenes. [ABSTRACT FROM AUTHOR]