基于修补的深度学习模型用于修复受探测器间隙影响的布拉格相干衍射图样
Patching-based Deep Learning model for the Inpainting of Bragg Coherent Diffraction patterns affected by detectors' gaps
AI总结：
提出基于修补的深度学习模型，利用裁剪数据训练并沿间隙修补预测，以修复BCDI图样中的探测器间隙，提升重建可靠性。
摘要：
我们提出了一种深度学习算法，用于修复受探测器间隙影响的布拉格相干衍射成像（BCDI）图样。这些缺失强度的区域可能损害重建算法的准确性，在最终结果中引入伪影。因此，恢复这些区域的强度对于确保更可靠的重建是可取的。我们方法的关键方面在于选择使用实验衍射数据和模拟数据的裁剪部分来训练神经网络，随后将模型生成的预测沿间隙进行修补，从而完成整个衍射峰。这为我们提供了更多的实验训练数据，并且由于尺寸有限，允许更快的模型训练，而神经网络可以应用于任意更大的BCDI数据集。此外，我们的方法不仅拓宽了应用范围，而且在面对具有挑战性的实验条件时，确保了数据完整性和可靠性的保持。
英文摘要：
We propose a deep learning algorithm for the inpainting of Bragg Coherent Diffraction Imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artifacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more reliable reconstructions. The key aspect of our method lies in the choice of training the neural network with cropped sections of both experimental diffraction data and simulated data and subsequently patching the predictions generated by the model along the gap, thus completing the full diffraction peak. This provides us with more experimental training data and allows for a faster model training due to the limited size, while the neural network can be applied to arbitrarily larger BCDI datasets. Moreover, our method not only broadens the scope of application but also ensures the preservation of data integrity and reliability in the face of challenging experimental conditions.
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