Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Many video enhancement algorithms rely on optical flow to register frames in a video
sequence. Precise flow estimation is however intractable; and optical flow itself is often a …

In this paper, we propose a state-of-the-art video denoising algorithm based on a
convolutional neural network architecture. Until recently, video denoising with neural …

Video deblurring is a challenging task due to the spatially variant blur caused by camera
shake, object motions, and depth variations, etc. Existing methods usually estimate optical …

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range
of exposures, which is important in image processing, computer graphics, and computer …

Modeling and synthesizing image noise is an important aspect in many computer vision
applications. The long-standing additive white Gaussian and heteroscedastic (signal …

This work focuses on the extremely low-light image enhancement, which aims to improve
image brightness and reveal hidden information in darken areas. Recently, image …

We present a simple and effective deep convolutional neural network (CNN) model for video
deblurring. The proposed algorithm mainly consists of optical flow estimation from …