利用卷积神经网络从不规则粗糙颗粒的干涉图像重建其形状
Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network
AI总结：
本文提出基于UNET残差模块的卷积神经网络，从DMD生成颗粒的干涉图像中高精度重建不规则粗糙颗粒的二维形状，并可结合三个视角实现三维重建。
摘要：
我们开发了一种卷积神经网络（CNN），用于从不规则粗糙颗粒的干涉图像重建其形状。该CNN基于带有残差块模块的UNET架构。数据库是利用在数字微镜器件（DMD）上编程的、完全已知的伪颗粒在激光照明下产生的实验图样构建的。该CNN在18000张实验干涉图像的基础上，使用AUSTRAL超级计算机（位于诺曼底CRIANN）进行了训练。该CNN在中心对称（棒状、十字形、枝晶状）和非中心对称（如T形、Y形或L形）颗粒的情况下进行了测试。所编程颗粒的尺寸和三维取向是随机的。CNN以良好的精度重建了不同形状。利用三个视角，可以进一步根据三个重建面完成颗粒的三维重建。
英文摘要：
We have developed a convolutional neural network (CNN) to reconstruct the shape of irregular rough particles from their interferometric images. The CNN is based on a UNET architecture with residual block modules. The database has been constructed using the experimental patterns generated by perfectly known pseudo-particles programmed on a Digital Micromirror Device (DMD) and under laser illumination. The CNN has been trained on a basis of 18000 experimental interferometric images using the AUSTRAL super computer (at CRIANN in Normandy). The CNN is tested in the case of centrosymmetric (stick, cross, dendrite) and non-centrosymmetric (like T, Y or L) particles. The size and the 3D orientation of the programmed particles are random. The different shapes are reconstructed by the CNN with good accuracy. Using three angles of view, the 3D reconstruction of particles from three reconstructed faces can be further done.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
