Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

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 …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Image restoration under adverse weather conditions has been of significant interest for
various computer vision applications. Recent successful methods rely on the current …

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …

Haze-free images are the prerequisites of many vision systems and algorithms, and thus
single image dehazing is of paramount importance in computer vision. In this field, prior …

Deep learning-based methods have achieved remarkable success in image restoration and
enhancement, but are they still competitive when there is a lack of paired training data? As …

In this paper, we reduce the image dehazing problem to an image-to-image translation
problem, and propose Enhanced Pix2pix Dehazing Network (EPDN), which generates a …

In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image
dehazing problem, which does not require pairs of hazy and corresponding ground truth …