Towards All Weather and Unobstructed Multi-Spectral Image Stitching: Algorithm and Benchmark

Image stitching is a fundamental task that requires multiple images from different viewpoints to generate a wide field-of-viewing (FOV) scene. Previous methods are developed on RGB images. However, the severe weather and harsh conditions, such as rain, fog, low light, strong light, etc., on visible images may introduce evident interference, leading to the distortion and misalignment of the stitched results. To remedy the deficient imaging of optical sensors, we investigate the complementarity across infrared and visible images to improve the perception of scenes in terms of visual information and viewing ranges. Instead of the cascaded fusion-stitching process, where the inaccuracy accumulation caused by image fusion hinders the stitch performance, especially content loss and ghosting effect, we develop a learnable feature adaptive network to investigate a stitch-oriented feature representation and perform the information complementary at the feature-level. By introducing a pyramidal structure along with the global fast correlation regression, the quadrature attention based correspondence is more responsible for feature alignment, and the estimation of sparse offsets can be realized in a coarse-to-fine manner. Furthermore, we propose the first infrared and visible image based multi-spectral image stitching dataset, covering a more comprehensive range of scenarios and diverse viewing baselines. Extensive experiments on real-world data demonstrate that our method reconstructs the wide FOV images with more credible structure and complementary information against state-of-the-arts.