Abstract: In response to the problems of edge information blur and poor local interference information processing capability in the digestive endoscopy diagnosis and treatment system, this paper proposes a single-depth estimation method based on dual attention cycle generative adversarial networks (DA-CycleGAN) to achieve accurate estimation of digestive depth information. By utilizing the global attention module to improve network accuracy and reduce information loss through global cross-dimensional interaction, as well as using the channel attention module to enhance the correlation between channels and reduce local interference information in endoscopic images, the discriminator adopts multi-scale feature fusion to improve discrimination ability and balance the performance of the generator and discriminator. The results show that the proposed method can predict good results in digestive endoscopy scenes, with an average accuracy improvement of 1.74%, 10.84%, and 0.69% for gastric, small intestinal, and colon datasets, respectively, compared to other unsupervised methods.

请帮我翻译以下一段话成英文专业性强一点:摘 要	针对消化道内窥镜诊疗系统存在边缘信息模糊以及对局部干扰信息处理能力差的问题提出一种双重注意力循环生成对抗网络Dual Attention CycleGAN DA-CyceGAN的单目深度估计方法以实现对消化道深度信息的准确估计。利用全局注意力模块中全局跨维度间的交互作用提高网络精度减少信息缺失;同时利用通道注意力模块提升通道之问的相关性减少内窥镜图像

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