Image Stitching in Adverse Condition: A Bidirectional-Consistency Learning Framework and Benchmark

Zengxi Zhang, Junchen Ge, Zhiying Jiang, Miao Zhang, Jinyuan Liu

Keywords: image stitching, deep learning, computervision

Abstract: Deep learning-based image stitching methods have achieved promising performance on conventional stitching datasets. However, real-world scenarios may introduce challenges such as complex weather conditions, illumination variations, and dynamic scene motion, which severely degrade image quality and lead to significant misalignment in stitching results. To solve this problem, we propose an adverse condition-tolerant image stitching network, dubbed ACDIS. We first introduce a bidirectional consistency learning framework, which ensures reliable alignment through an iterative optimization paradigm that integrates differentiable image restoration and Gaussian-distribute encoded homography estimation. Subsequently, we incorporate motion constraints into the seamless composition network to produce robust stitching results without interference from moving scenes. We further propose the first adverse scene image stitching dataset, which covers diverse parallax and scenes under low-light, haze, and underwater environments. Extensive experiments show that the proposed method can generate visually pleasing stitched images under adverse conditions, outperforming state-of-the-art methods.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Flagged For Ethics Review: true

Submission Number: 14365