Existing frameworks for image stitching often provide visually reasonable stitchings. However, they suffer from blurry artifacts and disparities in illumination, depth level, etc. Although the recent learning-based stitchings relax such disparities, the required methods impose sacrifice of image qualities failing to capture high-frequency details for stitched images. To address the problem, we propose a novel approach, implicit Neural Image Stitching (NIS) that extends arbitrary-scale super-resolution. Our method estimates Fourier coefficients of images for quality-enhancing warps. Then, the suggested model blends color mismatches and misalignment in the latent space and decodes the features into RGB values of stitched images. Our experiments show that our approach achieves improvement in resolving the low-definition imaging of the previous deep image stitching with favorable accelerated image-enhancing methods. Our source code is available at https://github.com/minshu-kim/NIS.

翻译：现有图像拼接框架通常能提供视觉上合理的拼接结果，但存在模糊伪影以及光照、深度层次等差异问题。尽管近期基于学习的拼接方法缓解了此类差异，但所需方法会牺牲图像质量，无法捕获拼接图像的高频细节。为解决该问题，我们提出一种新颖方法——隐含神经图像拼接（Implicit Neural Image Stitching, NIS），该方法扩展了任意尺度超分辨率。我们的方法通过估计图像的傅里叶系数实现质量增强的扭曲变换，随后所提出的模型在隐空间中融合色彩不匹配与未对齐问题，并将特征解码为拼接图像的RGB值。实验表明，与以往需加速图像增强方法的深度图像拼接相比，本方法在解决低分辨率成像方面取得了改进。我们的源代码已公开于https://github.com/minshu-kim/NIS。