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 …

Deep learning technologies have been applied in Single Image Dehazing (SID) tasks
successfully. However, most SID algorithms seldom consider to refine image details during …

Restoring high-quality images from degraded hazy observations is a fundamental and
essential task in the field of computer vision. While deep models have achieved significant …

Image dehazing is quite challenging in dense-haze scenarios, where quite less original
information remains in the hazy image. Though previous methods have made marvelous …

Underwater images suffer from light refraction and absorption, which impairs visibility and
interferes the subsequent applications. Existing underwater image enhancement methods …

It is well-known that image quality assessment usually meets with the problem of perception-
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …

Reducing the atmospheric haze and enhancing image clarity is crucial for computer vision
applications. The lack of real-life hazy ground truth images necessitates synthetic datasets …

Adverse weather conditions such as haze, rain, and snow often impair the quality of
captured images, causing detection networks trained on normal images to generalize poorly …

Image dehazing is a research focus, however, the existing methods do not make enough
use of depth information, which leads to poor dehazing effects for large-depth scenes …

Existing deep image dehazing methods usually depend on supervised learning with a large
number of hazy-clean image pairs which are expensive or difficult to collect. Moreover …