Many computer vision techniques infer properties of our physical world from images.
Although images are formed through the physics of light and mechanics, computer vision …

The presence of haze in the atmosphere degrades the quality of images captured by visible
camera sensors. The removal of haze, called dehazing, is typically performed under the …

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

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 …

Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

Research on neural radiance fields (NeRFs) for novel view generation is exploding with new
models and extensions. However, a question that remains unanswered is what happens in …

We propose an end-to-end trainable Convolutional Neural Network (CNN), named
GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules …

Saturation information in hazy images is conducive to effective haze removal, However,
existing saturation-based dehazing methods just focus on the saturation value of each pixel …

Adverse weather conditions in real-world scenarios lead to performance degradation of
deep learning-based detection models. A well-known method is to use image restoration …

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 …