Due to the fast inference and good performance, discriminative learning methods have been
widely studied in image denoising. However, these methods mostly learn a specific model …

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 …

In recent years, denoising methods based on deep learning have achieved unparalleled
performance at the cost of large computational complexity. In this work, we propose an …

Enhancing low light videos, which consists of denoising and brightness adjustment, is an
intriguing but knotty problem. Under low light condition, due to high sensitivity camera …

We propose a two-step algorithm that automatically estimates the noise level function of
stationary noise from a single image, ie, the noise variance as a function of the image …

In this paper, a methodology is investigated for signal recovery in the presence of non-
Gaussian noise. In contrast with regularized minimization approaches often adopted in the …

Given a set of image denoisers, each having a different denoising capability, is there a
provably optimal way of combining these denoisers to produce an overall better result? An …

In this paper, we address the problem of modeling a printing-imaging channel built on a
machine learning approach aka digital twin for anti-counterfeiting applications based on …

Deep convolutional neural networks (CNNs) have achieved huge success in the image
denoising fields. However, there still have three drawbacks to be overcome: firstly, it is a big …

The phase sensitivity limit of Differential Phase Contrast (DPC) with partially coherent light is
analyzed in details. The parameters to tune phase sensitivity, such as the diameter of …