Search Results for author: Alexander Krull

Found 18 papers, 12 papers with code

Direct Unsupervised Denoising

no code implementations27 Oct 2023 Benjamin Salmon, Alexander Krull

They learn to predict a central tendency of the posterior distribution over possible clean images.

Denoising

Unsupervised Structured Noise Removal with Variational Lossy Autoencoder

1 code implementation11 Oct 2023 Benjamin Salmon, Alexander Krull

In this paper, we present the first unsupervised deep learning-based denoiser that can remove this type of noise without access to any clean images or a noise model.

Image Denoising

Image Denoising and the Generative Accumulation of Photons

1 code implementation13 Jul 2023 Alexander Krull, Hector Basevi, Benjamin Salmon, Andre Zeug, Franziska Müller, Samuel Tonks, Leela Muppala, Ales Leonardis

This new perspective allows us to make three contributions: We present a new strategy for self-supervised denoising, We present a new method for sampling from the posterior of possible solutions by iteratively sampling and adding small numbers of photons to the image.

Image Denoising

uSplit: Image Decomposition for Fluorescence Microscopy

1 code implementation ICCV 2023 Ashesh Ashesh, Alexander Krull, Moises Di Sante, Francesco Pasqualini, Florian Jug

We present mSplit, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.

μSplit: efficient image decomposition for microscopy data

1 code implementation23 Nov 2022 Ashesh, Alexander Krull, Moises Di Sante, Francesco Silvio Pasqualini, Florian Jug

We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.

Improving Blind Spot Denoising for Microscopy

2 code implementations19 Aug 2020 Anna S. Goncharova, Alf Honigmann, Florian Jug, Alexander Krull

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.

Denoising

DenoiSeg: Joint Denoising and Segmentation

1 code implementation6 May 2020 Tim-Oliver Buchholz, Mangal Prakash, Alexander Krull, Florian Jug

Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.

Denoising Few-Shot Learning +1

Leveraging Self-supervised Denoising for Image Segmentation

1 code implementation27 Nov 2019 Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit, Pavel Tomancak, Florian Jug, Alexander Krull

Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images.

Denoising Image Segmentation +2

Fully Unsupervised Probabilistic Noise2Void

1 code implementation27 Nov 2019 Mangal Prakash, Manan Lalit, Pavel Tomancak, Alexander Krull, Florian Jug

Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.

Image Denoising

Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

3 code implementations3 Jun 2019 Alexander Krull, Tomas Vicar, Florian Jug

Self-supervised methods are, unfortunately, not competitive with models trained on image pairs.

Image Denoising

Noise2Void - Learning Denoising from Single Noisy Images

6 code implementations CVPR 2019 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.

Image Denoising

Global Hypothesis Generation for 6D Object Pose Estimation

no code implementations CVPR 2017 Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother

Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.

6D Pose Estimation using RGB Object

DSAC - Differentiable RANSAC for Camera Localization

4 code implementations CVPR 2017 Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother

The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.

Camera Localization Visual Localization

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image

no code implementations CVPR 2016 Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.

6D Pose Estimation 6D Pose Estimation using RGB +2

Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

no code implementations ICCV 2015 Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother

This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.

6D Pose Estimation 6D Pose Estimation using RGB +1

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