Multi-Spectral Image Stitching via Spatial Graph Reasoning

• URL: http://arxiv.org/abs/2307.16741v1
• Date: Mon, 31 Jul 2023 15:04:52 GMT
• Title: Multi-Spectral Image Stitching via Spatial Graph Reasoning
• Authors: Zhiying Jiang, Zengxi Zhang, Jinyuan Liu, Xin Fan, Risheng Liu
• Abstract summary: We propose a spatial graph reasoning based multi-spectral image stitching method. We embed multi-scale complementary features from the same view position into a set of nodes. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features.
• Score: 52.27796682972484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Networks (GCNs) in modeling feature relationships, we propose a spatial graph reasoning based multi-spectral image stitching method that effectively distills the deformation and integration of multi-spectral images across different viewpoints. To accomplish this, we embed multi-scale complementary features from the same view position into a set of nodes. The correspondence across different views is learned through powerful dense feature embeddings, where both inter- and intra-correlations are developed to exploit cross-view matching and enhance inner feature disparity. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features, generating informative and reliable wide FOV scenes. Moreover, we release a challenging dataset named ChaMS, comprising both real-world and synthetic sets with significant parallax, providing a new option for comprehensive evaluation. Extensive experiments demonstrate that our method surpasses the state-of-the-arts.

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