This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images
(restoration of rich details in hazy image). We focus on the proposed solutions and their …

This work reviews the results of the NTIRE 2021 Challenge on Non-Homogeneous
Dehazing. The proposed techniques and their results have been evaluated on a novel …

Image dehazing is a representative low-level vision task that estimates latent haze-free
images from hazy images. In recent years, convolutional neural network-based methods …

Considering the ill-posed nature, contrastive regularization has been developed for single
image dehazing, introducing the information from negative images as a lower bound …

In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …

To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image
pairs, many recent methods attempted to improve models' generalization ability by training …

Single image dehazing is a challenging ill-posed problem due to the severe information
degeneration. However, existing deep learning based dehazing methods only adopt clear …

Removing adverse weather conditions like rain, fog, and snow from images is an important
problem in many applications. Most methods proposed in the literature have been designed …

Existing dehazing approaches struggle to process real-world hazy images owing to the lack
of paired real data and robust priors. In this work, we present a new paradigm for real image …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …