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Image stitching is a technology that combines multiple images taken by different cameras to create a larger field of view. It has wide applications in scenarios such as surveillance and virtual reality, making it an important topic in computer vision. This paper addresses the challenges associated with stitching ground images with inconspicuous features using traditional methods. These challenges include poor feature extraction capabilities and issues like misalignment, artifacts, and structural deformations introduced during stitching. The paper proposes the utilization of unsupervised learning techniques to enhance the quality of image stitching. The network primarily consists of two parts: image alignment and image reconstruction. In the image reconstruction part, deformation rules for image stitching are learned through both a low-resolution branch and a high-resolution branch. Finally, it evaluates the stitched images before and after improvement using image stitching evaluation metrics. Experimental results demonstrate that this approach successfully mitigates artifacts and distortions introduced during stitching, resulting in an improved image stitching quality.
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