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
this https URL

提出一种新颖方法 —— 隐式神经图像拼接（NIS），通过估计图像的傅里叶系数进行质量增强的扭曲，以解决现有图像拼接方法中的模糊伪影和不同的光照、深度等差异问题，进而在保留高频细节的同时实现快速图像增强。