Automated retinal image analysis holds prime significance in the accurate diagnosis of
various critical eye diseases that include diabetic retinopathy (DR), age-related macular …

Deep learning is becoming an increasingly important tool for image reconstruction in
fluorescence microscopy. We review state-of-the-art applications such as image restoration …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

Lacking realistic ground truth data, image denoising techniques are traditionally evaluated
on images corrupted by synthesized iid Gaussian noise. We aim to obviate this unrealistic …

Noise is always presents in digital images during image acquisition, coding, transmission,
and processing steps. Noise is very difficult to remove it from the digital images without the …

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to
its inherently weak signal, fluorescence microscopy is not only much noisier than …

Robust generalization aims to tackle the most challenging data distributions which are rare
in the training set and contain severe noises, ie, photon-limited corruptions. Common …

Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising.
They are traditionally trained on pairs of images, which are often hard to obtain for practical …

Many digital imaging devices operate by successive photon-to-electron, electron-to-voltage,
and voltage-to-digit conversions. These processes are subject to various signal-dependent …

Deep networks provide state-of-the-art performance in multiple imaging inverse problems
ranging from medical imaging to computational photography. However, most existing …