We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis. Topaz-Denoise and pre-trained general models are now included in Topaz. We also present a general 3D denoising model for cryoET. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time.
Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. The general model we present is able to denoise new datasets without additional training. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Requisites should be installed beforehand.Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Results on DND Benchmark (Real Noisy Images) Traversing the conditional variable on a noisy image with noise-level 30. Right: Results on spatially variant noise. Left: Results on spectrally variant noise.Two neural networks are employed for two inference sub-problems. The problem can be reformulated into two sub-problems. Then, we can solve the MAP estimate with the given point estimate. When p(c|y) has a unimodal distribution with a sharp peak, the approximation is quite fair. We reformulate the log-posterior by introducing a new random variable c that contains prior based on human knowlegde.įor approximation the integration, we use the point estimate for c, which is argmax p(c|y). Notation: given noisy image y / latent clean image x.With our proposed method, we can successfully remove blind and real-world noise, with a moderate number of parameters of universal CNN.īrief Description of Our Proposed Method Probabilistic View As the CNN is a powerful tool for inference, our method is rooted in CNNs and propose a novel design of network for efficient inference. Concretely, we divide the blind image denoising problem into sub-problems and conquer each inference problem separately. In this paper, we present a CNN-based method that leverages the advantages of both methods based on the Bayesian perspective. On the other hand, traditional non-learning methods can involve explicit image priors, but they require considerable computation time and cannot exploit large-scale external datasets. Moreover, they cannot easily employ explicit priors. While the deep CNNs are powerful for removing the noise with known statistics, they tend to lack flexibility and practicality for the blind and real-world noise. Recently, deep convolutional neural networks (CNNs) have shown great success in image denoising by incorporating large-scale synthetic datasets.
Traditionally, many researchers have investigated image priors for the denoising, within the Bayesian perspective based on image properties and statistics. Image denoising is an essential part of many image processing and computer vision tasks due to inevitable noise corruption during image acquisition. DUBD Deep Universal Blind Image Denoising