In this paper, we propose a self-supervised twin network approach based on this a priori. The method of generating the approximate10 edge information of an image and then differentially eliminating the edge errors11 in the reconstructed image with a dilate algorithm. This is used to improve the12 accuracy of the reconstructed image and to separate foreign matter and noise from13 the original image, so that it can be visualized in a more practical scene

翻译：在本文中,我们建议基于这一先验的自我监督双网络方法。 生成图像近10边缘信息的方法, 并用扩展算法区别消除重建后的图像中的边缘差错 11 。 这种方法用来提高重建后的图像的12 准确度, 并将外在物质和噪音与13 原始图像分开, 以便在更实际的场景中可视化 。