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Found 20 papers, 13 papers with code
WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
Deep image registration has demonstrated exceptional accuracy and fast inference.
Can virtual staining for high-throughput screening generalize?
Generalization to unseen cell types shows variability depending on the cell type; models trained on ovarian or lung cell samples often perform well under other conditions, while those trained on breast cell samples consistently show poor generalization.
Direct Unsupervised Denoising
They learn to predict a central tendency of the posterior distribution over possible clean images.
Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise
Accurate analysis of microscopy images is hindered by the presence of noise.
Image Denoising and the Generative Accumulation of Photons
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.
uSplit: Image Decomposition for Fluorescence Microscopy
We present mSplit, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.
μSplit: efficient image decomposition for microscopy data
We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.
Improving Blind Spot Denoising for Microscopy
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.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
DenoiSeg: Joint Denoising and Segmentation
Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.
Leveraging Self-supervised Denoising for Image Segmentation
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images.
Fully Unsupervised Probabilistic Noise2Void
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.
Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Self-supervised methods are, unfortunately, not competitive with models trained on image pairs.
Noise2Void - Learning Denoising from Single Noisy Images
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.
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation.
Global Hypothesis Generation for 6D Object Pose Estimation
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.
DSAC - Differentiable RANSAC for Camera Localization
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.
Random Forests versus Neural Networks - What's Best for Camera Localization?
This work addresses the task of camera localization in a known 3D scene given a single input RGB image.
Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image
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.
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
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.
