Seam Mask Guided Partial Reconstruction with Quantum-Inspired Local Aggregation For Deep Image Stitching

Chen-Bin Feng, Jie Zhang, Jiaxue Li, Yicong Zhou

Abstract: In image stitching, artifacts caused by misalignment affect the visual quality and the performance of subsequent tasks such as segmentation and detection. This paper proposes SMPR, a reconstruction-based aligned image composition method to minimize artifacts. SMPR fuses images in part of the overlapping areas and reconstructs other portions from single images. Specifically, we propose a seam mask generation method to obtain optimal seam masks that pass through minimal misalignment. During training, we use the seam masks to guide the model in detecting optimal fusion areas. In testing, the model can detect fusion areas without seam masks and reconstruct stitching results. We propose a quantum-inspired local aggregation (QILA) module to improve feature reconstruction performance. We develop an encoder-decoder network with QILA and experiment on a real-world dataset. The experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative aspects.