Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. A fundamental step in the recovering of the 3D single-particle structure is to align its 2D projections; thus, the construction of a canonical representation with a fixed rotation angle is required. Most approaches use discrete clustering which fails to capture the continuous nature of image rotation, others suffer from low-quality image reconstruction. We propose a novel method that leverages the recent development in the generative adversarial networks. We introduce an encoder-decoder with a rotation angle classifier. In addition, we utilize a discriminator on the decoder output to minimize the reconstruction error. We demonstrate our approach with the Cryo-EM 5HDB and the rotated MNIST datasets showing substantial improvement over recent methods.

翻译：Cryo-Eectron 显微镜(Cryo-EM)是确定近原子分辨率颗粒的3D结构的诺贝尔奖得主技术。恢复3D单粒结构的一个基本步骤是调整其2D预测;因此,需要建造一个固定旋转角度的圆柱形图解。大多数方法使用离散集,无法捕捉图像旋转的连续性,而其他方法则受到低质量图像重建的影响。我们提出了一个新颖的方法,利用基因对抗网络最近的发展。我们引入了一个带有旋转角度分类器的编码器解码器。此外,我们利用解码器输出的区分器来尽量减少重建错误。我们用加密-EM 5HDB 和旋转的MNIST数据集展示了我们的方法,这些方法比最近的方法有了很大的改进。