5 citations · 6 across the 3 of their papers we have counts for
6 papers · 1 filter
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),…
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…
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Mangal Prakash, Alexander Krull, Florian Jug
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware…
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
Stefan Haller, Mangal Prakash, Lisa Hutschenreiter +5
We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in man…
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…
Crowd Sourcing Image Segmentation with iaSTAPLE
Dmitrij Schlesinger, Florian Jug, Gene Myers +2
We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label…