Noise2sr: Learning to denoise from super-resolved single noisy fluorescence image
Fluorescence microscopy is a key driver to promote discoveries of biomedical research.
However, with the limitation of microscope hardware and characteristics of the observed …
However, with the limitation of microscope hardware and characteristics of the observed …
Removing structured noise with self-supervised blind-spot networks
Removal of noise from fluorescence microscopy images is an important first step in many
biological analysis pipelines. Current state-of-the-art supervised methods employ …
biological analysis pipelines. Current state-of-the-art supervised methods employ …
Fluorescence microscopy images denoising via deep convolutional sparse coding
Fluorescence microscopy images captured in low light and short exposure time conditions
are always contaminated by photons and readout noises, which reduce the fluorescence …
are always contaminated by photons and readout noises, which reduce the fluorescence …
Noise suppression with similarity-based self-supervised deep learning
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and
photon-counting computed tomography (CT) denoising can optimize diagnostic …
photon-counting computed tomography (CT) denoising can optimize diagnostic …
Blind denoising of fluorescence microscopy images using GAN-based global noise modeling
L Zhong, G Liu, G Yang - 2021 IEEE 18th International …, 2021 - ieeexplore.ieee.org
Fluorescence microscopy is a key driving force behind advances in modern life sciences.
However, due to constraints in image formation and acquisition, to obtain high signal-to …
However, due to constraints in image formation and acquisition, to obtain high signal-to …
M-Denoiser: Unsupervised image denoising for real-world optical and electron microscopy data
Real-world microscopy data have a large amount of noise due to the limited light/electron
that can be used to capture images. The noise of microscopy data is composed of signal …
that can be used to capture images. The noise of microscopy data is composed of signal …
Unsupervised Structured Noise Removal with Variational Lossy Autoencoder
Most unsupervised denoising methods are based on the assumption that imaging noise is
either pixel-independent, ie, spatially uncorrelated, or signal-independent, ie, purely …
either pixel-independent, ie, spatially uncorrelated, or signal-independent, ie, purely …
[PDF][PDF] Multi-Scale Self-Attention Network for Denoising Medical Images
Deep learning-based image denoising plays a critical role in medical imaging, especially
when dealing with rapid fluorescence and ultrasound captures where traditional noise …
when dealing with rapid fluorescence and ultrasound captures where traditional noise …
Improving blind spot denoising for microscopy
AS Goncharova, A Honigmann, F Jug… - European Conference on …, 2020 - Springer
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 …
consequently challenged by the resulting levels of noise in the acquired images. This …
Self-supervised poisson-gaussian denoising
We extend the blindspot model for self-supervised denoising to handle Poisson-Gaussian
noise and introduce an improved training scheme that avoids hyperparameters and adapts …
noise and introduce an improved training scheme that avoids hyperparameters and adapts …