This paper reviews the first challenge on image dehazing (restoration of rich details in hazy
image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 …

Convolutional neural networks (CNNs) have achieved significant success in the single
image dehazing task. Unfortunately, most existing deep dehazing models have high …

Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in
captured images. Existing imaging devices lack the ability to effectively and efficiently …

Image dehazing is an ill-posed problem that has been extensively studied in the recent
years. The objective performance evaluation of the dehazing methods is one of the major …

This paper reviews the second challenge on image dehazing (restoration of rich details in
hazy image) with focus on proposed solutions and results. The training data consists from 55 …

Single image dehazing is an ill-posed problem that has recently drawn important attention.
Despite the significant increase in interest shown for dehazing over the past few years, the …

Robust recovery of lost colors in underwater images remains a challenging problem. We
recently showed that this was partly due to the prevalent use of an atmospheric image …

Single image dehazing is a challenging task, for which the domain shift between synthetic
training data and real-world testing images usually leads to degradation of existing methods …

On benchmark images, modern dehazing methods are able to achieve very comparable
results whose differences are too subtle for people to qualitatively judge. Thus, it is …

Due to the complexity of real environment and potential defects of current simulation
datasets, either prior-based or deep learning-based single image dehazing methods may …