BACKGROUND To perform a three-dimensional (3-D) reconstruction of electron cryomicroscopy (cryo-EM) images of viruses, it is necessary to determine the similarity of image blocks of the two-dimensional (2-D) projections of the virus. The projections containing high resolution information are typically very noisy. Instead of the traditional Euler metric, this paper proposes a new method, based on the geodesic metric, to measure the similarity of blocks. RESULTS Our method is a 2-D image denoising approach. A data set of 2243 cytoplasmic polyhedrosis virus (CPV) capsid particle images in different orientations was used to test the proposed method. Relative to Block-matching and three-dimensional filtering (BM3D), Stein's unbiased risk estimator (SURE), Bayes shrink and K-means singular value decomposition (K-SVD), the experimental results show that the proposed method can achieve a peak signal-to-noise ratio (PSNR) of 45.65. The method can remove the noise from the cryo-EM image and improve the accuracy of particle picking. CONCLUSIONS The main contribution of the proposed model is to apply the geodesic distance to measure the similarity of image blocks. We conclude that manifold learning methods can effectively eliminate the noise of the cryo-EM image and improve the accuracy of particle picking.

中文翻译：

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基于测地距离的低温电子显微镜图像降噪。

背景技术为了执行病毒的电子低温显微镜（cryo-EM）图像的三维（3-D）重建，有必要确定病毒的二维（2-D）投影的图像块的相似性。包含高分辨率信息的投影通常非常嘈杂。代替传统的欧拉度量，本文提出了一种基于测地度量的新方法来测量块的相似性。结果我们的方法是一种二维图像去噪方法。数据集以不同方向的2243胞质多角体病毒（CPV）衣壳颗粒图像的数据集来测试该方法。相对于块匹配和三维过滤（BM3D），斯坦因的无偏风险估计器（SURE），贝叶斯收缩和K均值奇异值分解（K-SVD），实验结果表明，该方法可以达到45.65的峰值信噪比（PSNR）。该方法可以去除冷冻EM图像中的噪声并提高粒子拾取的准确性。结论所提出模型的主要贡献是应用测地距离来测量图像块的相似性。我们得出的结论是，多种学习方法可以有效消除cryo-EM图像的噪声并提高粒子拾取的准确性。