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 …
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 …
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 …
Whitenner-blind image denoising via noise whiteness priors
S Izadi, Z Mirikharaji, M Zhao… - Proceedings of the …, 2019 - openaccess.thecvf.com
The accuracy of medical imaging-based diagnostics is directly impacted by the quality of the
collected images. A passive approach to improve image quality is one that lags behind …
collected images. A passive approach to improve image quality is one that lags behind …
GAN2GAN: Generative noise learning for blind denoising with single noisy images
We tackle a challenging blind image denoising problem, in which only single distinct noisy
images are available for training a denoiser, and no information about noise is known …
images are available for training a denoiser, and no information about noise is known …
[PDF][PDF] Gan2gan: Generative noise learning for blind image denoising with single noisy images
We tackle a challenging blind image denoising problem, in which only single noisy images
are available for training a denoiser and no information about noise is known, except for it …
are available for training a denoiser and no information about noise is known, except for it …
Image denoising for fluorescence microscopy by self-supervised transfer learning
When using fluorescent microscopy to study cellular dynamics, trade-offs typically have to be
made between light exposure and quality of recorded image to balance phototoxicity and …
made between light exposure and quality of recorded image to balance phototoxicity and …
Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network
FHG Zuluaga, F Bardozzo, JIR Patino… - 2021 43rd Annual …, 2021 - ieeexplore.ieee.org
In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality
of image analysis. In general, the accuracy of this process may depend both on the …
of image analysis. In general, the accuracy of this process may depend both on the …
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 …
Noise2Fast: fast self-supervised single image blind denoising
In the last several years deep learning based approaches have come to dominate many
areas of computer vision, and image denoising is no exception. Neural networks can learn …
areas of computer vision, and image denoising is no exception. Neural networks can learn …