Sabetsarvestani Z, Sober B, Higgitt C, Daubechies I, Rodrigues M R D
Department of Electronic and Electrical Engineering, University College London, London, UK.
Department of Mathematics and Rhodes Information Initiative, Duke University, Durham, NC, USA.
Sci Adv. 2019 Aug 30;5(8):eaaw7416. doi: 10.1126/sciadv.aaw7416. eCollection 2019 Aug.
X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to "read." To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the and panels of the spectacularly improve on previous attempts.
多联画屏翅膀的X光图像，或绘制在其支撑物两面的其他艺术品的X光图像，在一张图像中包含了两面画作的内容，这使得专家们难以“解读”。为了提高这些X光图像在研究这些艺术品时的实用性，希望将内容分离成两张图像，每张图像只对应一面。这是一项艰巨的任务，之前的方法仅取得了部分成功。深度神经网络算法最近在广泛的图像分析和其他具有挑战性的任务中取得了显著进展。因此，我们提出了一种新的自监督方法来进行这种X光分离，利用现有的卷积神经网络架构；从[具体画作名称]的[具体面板名称]和[具体面板名称]中获取的细节结果比之前的尝试有了显著改进。
