Multi-Scale Image Stitching Based on Unsupervised Controllable Fusion Structure

Traditional feature-based image stitching techniques rely heavily on the quality of feature detection and often fail to get a high quality solution for images with fewer features. Generating high-quality stitching images with a natural structure is still a challenging task in computer vision. The current distortion-based image alignment relies on the division of the grid, which is currently mostly equally divided without reference to the information in the image itself, and thus there are some problems such as straight line retention in the alignment stage. On this basis, if the deep learning approach is added in the later reconstruction stage to limit the generation of artifacts at the pixel level, it will be greatly improved, and we can ensure the high quality of the final splicing result of the image through two stages. In this paper, we succeed in proposing a novelty model called multi-scale image stitching based on unsupervised controllable fusion module which can be divided in two stages: Multi-scale grids alignment structure based on feature points density and image reconstruction structure based on unsupervised controllable fusion structure. We develop new energy terms to adapt to limit the transformations of the new grids. Extensive experiments demonstrate that the proposed method outperforms most state-of-the-arts by effectively preserving the linear structure in the image and improving the robustness.