College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
College of Information Engineering, University of Macau, Macao, China.
PLoS One. 2024 Sep 18;19(9):e0310214. doi: 10.1371/journal.pone.0310214. eCollection 2024.
Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network's ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE_SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination.
图像拼接是一项传统但具有挑战性的计算机视觉任务。其目标是将具有重叠区域的多张图像拼接成一张自然、高分辨率、无重影或拼接痕迹的图像。本文旨在增加内窥镜的视野范围，降低漏检率。为此，提出了一种基于胃肠道无监督全景图像拼接的改进深度框架。此外，还介绍了用于单目内窥镜图像像差校正的预处理，并在图像重建网络中添加了 C2f 模块，以提高网络提取特征的能力。提出了一个全面的真实图像数据集 GASE-Dataset，用于建立无监督深度胃肠道图像拼接的评估基准和训练学习框架。实验结果表明，MSE、RMSE、PSNR、SSIM 和 RMSE_SW 指标得到了改善，而拼接时间仍在可接受的范围内。与传统的图像拼接方法相比，该方法的性能得到了增强。此外，还提出了改进措施，以解决基于监督学习的图像拼接方案中缺乏标注数据、泛化能力不足以及综合性能不足的问题。这些改进为胃肠道检查提供了有价值的帮助。
