Wu Haoze, Bao Chun, Hao Qun, Cao Jie, Zhang Li
Instrument Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130013, China.
Sensors (Basel). 2024 Aug 19;24(16):5352. doi: 10.3390/s24165352.
Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that combines the powerful feature extraction capabilities of the Superpoint algorithm and the exact feature matching capabilities of the Lightglue algorithm with the image fusion algorithm of Unsupervised Deep Image Stitching (UDIS). Our proposed method effectively improves the situation where the linear structure is distorted and the resolution is low in the stitching results of the UDIS algorithm. On this basis, we make up for the shortcomings of the UDIS fusion algorithm. For stitching fractures of UDIS in some complex situations, we optimize the loss function of UDIS. We use a second-order differential Laplacian operator to replace the difference in the horizontal and vertical directions to emphasize the continuity of the structural edges during training. Combined with the above improvements, the Super Unsupervised Deep Image Stitching (SuperUDIS) algorithm is finally formed. SuperUDIS has better performance in both qualitative and quantitative evaluations compared to the UDIS algorithm, with the PSNR index increasing by 0.5 on average and the SSIM index increasing by 0.02 on average. Moreover, the proposed method is more robust in complex environments with large color differences or multi-linear structures.
如今，大视野图像在各种环境中得到了越来越广泛的应用，图像拼接技术可以弥补硬件设计所导致的有限视野问题。然而，以往的方法在各种环境中都存在局限性。在本文中，我们提出了一种方法，该方法将Superpoint算法强大的特征提取能力、Lightglue算法精确的特征匹配能力与无监督深度图像拼接（UDIS）的图像融合算法相结合。我们提出的方法有效地改善了UDIS算法拼接结果中线性结构扭曲和分辨率低的情况。在此基础上，我们弥补了UDIS融合算法的不足。对于UDIS在某些复杂情况下的拼接裂缝，我们优化了UDIS的损失函数。我们使用二阶微分拉普拉斯算子来代替水平和垂直方向的差值，以在训练过程中强调结构边缘的连续性。结合上述改进，最终形成了超级无监督深度图像拼接（SuperUDIS）算法。与UDIS算法相比，SuperUDIS在定性和定量评估中都具有更好的性能，PSNR指标平均提高了0.5，SSIM指标平均提高了0.02。此外，所提出的方法在具有大色差或多线性结构的复杂环境中更具鲁棒性。
