An effective unsupervised image stitching method based on improved UDISNet
Hongru Wang, Jingtao Zhang, Chaolei Dai, Cheng Hu, Jia Wang · Engineering Research Express · 2025
Abstract Addressing the challenges of artifacts, misalignment, and distortion in complex scenarios, which arise from difficulties in feature extraction from images, we proposed a novel unsupervised image stitching method based on Unsupervised Deep Image Stitching Net (UDISNet). This method improves the UDISNet-based unsupervised image stitching method from two aspects: image alignment and image reconstruction. In the image alignment stage, to solve the problem that the network cannot extract effective alignment information in large baseline scenarios, the Edge-Preserved Image Stitching Net(EPISNet) large baseline depth homography network is introduced and an AdaPool layer is used between each two convolution layers. In the image reconstruction stage, to address the problems of weak image edge information detection, artifacts, and misalignment, we employ the Edge-Preserved Deformation Net (EPDNet) image edge detection module and deepen the network. The comparative experimental results show that the RMSE, PSNR, and SSIM of our method are 1.93, 24.85, and 0.85, respectively, which are all superior to those of other state-of-the-art methods. Moreover, extensive experiments indicate that our method can effectively stitch both ground and water surface images, which means that it has a high generalization. Therefore, our work is effective and constructive.