Dehaze-AGGAN: Unpaired remote sensing image dehazing using enhanced attention-guide generative adversarial networks
Remote sensing image dehazing is of great scientific interest and application value in both
military and civil fields. In this article, we propose an enhanced attention-guide generative …
military and civil fields. In this article, we propose an enhanced attention-guide generative …
FD-GAN: Generative adversarial networks with fusion-discriminator for single image dehazing
Recently, convolutional neural networks (CNNs) have achieved great improvements in
single image dehazing and attained much attention in research. Most existing learning …
single image dehazing and attained much attention in research. Most existing learning …
Memory-oriented unpaired learning for single remote sensing image dehazing
Remote sensing image dehazing (RSID) is an extremely challenging problem due to the
irregular and nonuniform distribution of haze. The existing RSID methods achieve excellent …
irregular and nonuniform distribution of haze. The existing RSID methods achieve excellent …
Single remote sensing image dehazing using a prior-based dense attentive network
Z Gu, Z Zhan, Q Yuan, L Yan - Remote Sensing, 2019 - mdpi.com
Remote sensing image dehazing is an extremely complex issue due to the irregular and non-
uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network …
uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network …
AGLC-GAN: Attention-based global-local cycle-consistent generative adversarial networks for unpaired single image dehazing
RS Jaisurya, S Mukherjee - Image and Vision Computing, 2023 - Elsevier
Image dehazing is a critical image pre-processing task to estimate the haze-free images
corresponding to the input hazy images. Despite the recent advances, the task of image …
corresponding to the input hazy images. Despite the recent advances, the task of image …
Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate
the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain …
the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain …
Fusion of heterogeneous adversarial networks for single image dehazing
In this paper, we propose a novel image dehazing method. Typical deep learning models for
dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be …
dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be …
Dehaze-GLCGAN: unpaired single image de-hazing via adversarial training
Z Anvari, V Athitsos - arXiv preprint arXiv:2008.06632, 2020 - arxiv.org
Single image de-hazing is a challenging problem, and it is far from solved. Most current
solutions require paired image datasets that include both hazy images and their …
solutions require paired image datasets that include both hazy images and their …
Single satellite optical imagery dehazing using SAR image prior based on conditional generative adversarial networks
B Huang, L Zhi, C Yang, F Sun… - Proceedings of the …, 2020 - openaccess.thecvf.com
Satellite image dehazing aims at precisely retrieving the real situations of the obscured parts
from the hazy remote sensing (RS) images, which is a challenging task since the hazy …
from the hazy remote sensing (RS) images, which is a challenging task since the hazy …
An attention encoder-decoder network based on generative adversarial network for remote sensing image dehazing
L Zhao, Y Zhang, Y Cui - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Remote sensing image dehazing is a difficult problem for its complex characteristics. It can
be regarded as the preprocessing of high-level tasks of remote sensing images. To remove …
be regarded as the preprocessing of high-level tasks of remote sensing images. To remove …