Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy

Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising photographic images. However, their potential has been inadequately explored for scientific imaging. Denoising CNNs are typically trained on clean images corrupted with artificial noise, but in scientific applications, noiseless ground-truth images are usually not available. To address this, we propose a simulation-based denoising (SBD) framework, in which CNNs are trained on simulated images. We test the framework on transmission electron microscopy (TEM) data, showing that it outperforms existing techniques on a simulated benchmark dataset, and on real data. We analyze the generalization capability of SBD, demonstrating that the trained networks are robust to variations of imaging parameters and of the underlying signal structure. Our results reveal that state-of-the-art architectures for denoising photographic images may not be well adapted to scientific-imaging data. For instance, substantially increasing their field-of-view dramatically improves their performance on TEM images acquired at low signal-to-noise ratios. We also demonstrate that standard performance metrics for photographs (such as peak signal-to-noise ratio) may not be scientifically meaningful, and propose several metrics to remedy this issue in the case of TEM images. In addition, we propose a technique, based on likelihood computations, to visualize the agreement between the structure of the denoised images and the observed data. Finally, we release a publicly available benchmark dataset containing 18,000 simulated TEM images.