53 citations · 67 across the 4 of their papers we have counts for
5 papers
Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning
Matan Leibovich, Mai Tan, Ramon Manzorro +4
We present a novel approach for extracting 3D atomic-level information from transmission electron microscopy (TEM) images affected by significant noise. The approach is based on fo…
Adaptive Denoising via GainTuning
Sreyas Mohan, Joshua L. Vincent, Ramon Manzorro +3
Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties g…
Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise
Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan +6
A deep convolutional neural network has been developed to denoise atomic-resolution TEM image datasets of nanoparticles acquired using direct electron counting detectors, for appli…
Unsupervised Deep Video Denoising
Dev Yashpal Sheth, Sreyas Mohan, Joshua L. Vincent +5
Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such…
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent +6
Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they p…