7 papers
Improving Blind Spot Denoising for Microscopy
Anna S. Goncharova, Alf Honigmann, Florian Jug +1
Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem i…
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…
Fully Unsupervised Probabilistic Noise2Void
Mangal Prakash, Manan Lalit, Pavel Tomancak +2
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing. A new category of DL methods such…
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…
Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Alexander Krull, Tomas Vicar, Florian Jug
Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practi…