151 citations · 220 across the 9 of their papers we have counts for
14 papers
Image Denoising and the Generative Accumulation of Photons
Alexander Krull, Hector Basevi, Benjamin Salmon +5
We present a fresh perspective on shot noise corrupted images and noise removal. By viewing image formation as the sequential accumulation of photons on a detector grid, we show th…
μSplit: efficient image decomposition for microscopy data
Ashesh, Alexander Krull, Moises Di Sante +2
We present μSplit, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images. We find that best results using regular deep architectures…
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…
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…
Global Hypothesis Generation for 6D Object Pose Estimation
Frank Michel, Alexander Kirillov, Eric Brachmann +4
This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local feat…
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
Alexander Krull, Eric Brachmann, Sebastian Nowozin +3
State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational reso…
DSAC - Differentiable RANSAC for Camera Localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin +4
RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines ha…
Random Forests versus Neural Networks - What's Best for Camera Localization?
Daniela Massiceti, Alexander Krull, Eric Brachmann +2
This work addresses the task of camera localization in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressin…
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Alexander Krull, Eric Brachmann, Frank Michel +3
Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. T…