Accelerating High-resolution Image Stitching for the Dual Camera System based on GPU

Image stitching plays an important role in the field of computer vision and image processing. Although some sophisticated stitching methods have been proposed, they are usually time-consuming. Because many application scenarios, such as video surveillance, need quick image stitching to provide a wide field of view, accelerating the image stitching is critical. In recent years, the neural network is used to stitching images, but only relatively small size images can be processed; meanwhile, time-consuming image stitching is also unavoidable. In this paper, to quickly stitch 1920×1080 images generated by the dual camera system, an image stitching algorithm is proposed. We divided the algorithm depending on its timeliness on different steps, and its time-consuming steps are parallelized on Graphics Processing Unit (GPU) platforms to be accelerated. We tested different hierarchies for organization of threads and access of memory on compute unified device architecture (CUDA) programming, and the final experiment results showed that our algorithm could reach a speedup ratio up to 119x on the Desktop with 3080ti.