IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Discrete Approximation Algorithm for the Radon Transform
SIAM Journal on Computing
Fast Calculation of Multiple Line Integrals
SIAM Journal on Scientific Computing
Parallel Computing Strategies for Determining Viral Capsid Structure by Cryoelectron Microscopy
IEEE Computational Science & Engineering
Orientation Refinement of Virus Structures with Unknown Symmetry
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Ab initio reconstruction and experimental design for cryo electron microscopy
IEEE Transactions on Information Theory
A stochastic kinematic model of class averaging in single-particle electron microscopy
International Journal of Robotics Research
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Cryo-electron microscopy has recently been recognized as a useful alternative to obtain three-dimensional density maps of macromolecular complexes, especially when crystallography and NMR techniques fail. The three-dimensional model is constructed from large collections of cryo-electron microscopy images of identical particles in random (and unknown) orientations. The major problem with cryo-electron microscopy is that the images are very noisy as the signal-to-noise ratio can be below one. Thus, standard filtering techniques are not directly applicable. Traditionally, the problem of immense noise in the cryo-electron microscopy images has been tackled by clustering the images and computing the class averages. However, then one has to assume that the particles have only few preferred orientations. In this paper we propose a sound method for denoising cryo-electron microscopy images using their Radon transforms. The method assumes only that the images are from identical particles but nothing is assumed about the orientations of the particles. Our preliminary experiments show that the method can be used to improve the image quality even when the signal-to-noise ratio is very low.