Image restoration aims to reconstruct a sharp image from its degraded counterpart, which
plays an important role in many fields. Recently, Transformer models have achieved …

Existing deep learning methods for image deblurring typically train models using pairs of
sharp images and their blurred counterparts. However, synthetically blurring images does …

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In
this paper, we propose a trainable hybrid network to enhance the visibility of such degraded …

With the development of convolutional neural networks, hundreds of deep learning–based
dehazing methods have been proposed. In this article, we provide a comprehensive survey …

Single image dehazing has been a challenging problem which aims to recover clear images
from hazy ones. The performance of existing image dehazing methods is limited by hand …

The performance of existing image dehazing methods is limited by hand-designed features,
such as the dark channel, color disparity and maximum contrast, with complex fusion …

We propose an effective image dehazing algorithm which explores useful information from
the input hazy image itself as the guidance for the haze removal. The proposed algorithm …

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater
images usually suffer from color distortion and blurred details. However, due to the limited …

Existing enhancement methods are empirically expected to help the high-level end
computer vision task: however, that is observed to not always be the case in practice. We …

Video interpolation is an important problem in computer vision, which helps overcome the
temporal limitation of camera sensors. Existing video interpolation methods usually assume …