12 citations · 21 across the 4 of their papers we have counts for
6 papers
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture
Eva Höck, Tim-Oliver Buchholz, Anselm Brachmann +2
In recent years, neural network based image denoising approaches have revolutionized the analysis of biomedical microscopy data. Self-supervised methods, such as Noise2Void (N2V),…
Fourier Image Transformer
Tim-Oliver Buchholz, Florian Jug
Transformer architectures show spectacular performance on NLP tasks and have recently also been used for tasks such as image completion or image classification. Here we propose to…
DenoiSeg: Joint Denoising and Segmentation
Tim-Oliver Buchholz, Mangal Prakash, Alexander Krull +1
Microscopy image analysis often requires the segmentation of objects, but training data for this task is typically scarce and hard to obtain. Here we propose DenoiSeg, a new method…
Leveraging Self-supervised Denoising for Image Segmentation
Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit +3
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copiou…
Noise2Void - Learning Denoising from Single Noisy Images
Alexander Krull, Tim-Oliver Buchholz, Florian Jug
The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images. Recently it has been s…
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data
Tim-Oliver Buchholz, Mareike Jordan, Gaia Pigino +1
Multiple approaches to use deep learning for image restoration have recently been proposed. Training such approaches requires well registered pairs of high and low quality images.…