Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …

Fluorescence microscopy imaging speed is fundamentally limited by the measurement
signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate …

Deep convolutional neural networks (CNNs) for video denoising are typically trained with
supervision, assuming the availability of clean videos. However, in many applications, such …

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 …

Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural
networks (CNNs) provide the current state of the art in denoising photographic images …

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 …

Machine learning (ML) methods are extraordinarily successful at denoising photographic
images. The application of such denoising methods to scientific images is, however, often …

Denoising and demosaicking are two essential steps to reconstruct a clean full-color image
from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images …

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due
to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and …

In diverse microscopy modalities, sensors measure only real-valued intensities. Additionally,
the sensor readouts are affected by Poissonian-distributed photon noise. Traditional …