Results 11 to 20 of about 3,779 (211)

Aerial Image Dehazing Using Reinforcement Learning

Remote Sensing, 2022
Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing.
Jing Yu, Deying Liang, Bo Hang, Hongtao Gao   +3 more
doaj   +2 more sources

Deep guided transformer dehazing network

Scientific Reports, 2023
Single image dehazing has received a lot of concern and achieved great success with the help of deep-learning models. Yet, the performance is limited by the local limitation of convolution.
Shengdong Zhang, Liping Zhao, Keli Hu, Sheng Feng, En Fan, Li Zhao   +5 more
doaj   +2 more sources

Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing

Journal of Imaging, 2023
Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks.
Zhibo Wang, Jia Jia, Peng Lyu, Jeongik Min   +3 more
doaj   +2 more sources

Atmospheric Light Estimation Using Polarization Degree Gradient for Image Dehazing

Sensors
A number of image dehazing techniques depend on the estimation of atmospheric light intensity. The majority of dehazing algorithms do not incorporate a physical model to estimate atmospheric light, leading to reduced accuracy and significantly impacting ...
Shuai Liu, Hang Li, Jinyu Zhao, Junchi Liu, Youqiang Zhu, Zhenduo Zhang   +5 more
doaj   +2 more sources

High-Resolution Representations Network for Single Image Dehazing

Sensors, 2022
Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose
Wensheng Han, Hong Zhu, Chenghui Qi, Jingsi Li, Dengyin Zhang   +4 more
doaj   +2 more sources

Autonomous Single-Image Dehazing: Enhancing Local Texture with Haze Density-Aware Image Blending

Remote Sensing
Single-image dehazing is an ill-posed problem that has attracted a myriad of research efforts. However, virtually all methods proposed thus far assume that input images are already affected by haze. Little effort has been spent on autonomous single-image
Siyeon Han, Dat Ngo, Yeonggyu Choi, Bongsoon Kang   +3 more
doaj   +2 more sources

Multiscale implicit frequency selective network for single-image dehazing

ETRI Journal
Image dehazing is aimed to reconstruct a clear latent image from a degraded image affected by haze. Although vision transformers have achieved impressive success in various computer vision tasks, the limitations in scale and quality of available datasets
Zhibo Wang, Jia Jia, Jeongik Min
doaj   +2 more sources

ED-Dehaze Net: Encoder and Decoder Dehaze Network.

International Journal of Interactive Multimedia and Artificial Intelligence, 2022
The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which contains a Generator and a Discriminator. In particular, the Generator uses an Encoder-Decoder structure to
Zhang, Hongqi, Wei, Yixiong, Zhou, Hongqiao, Wu, Qianhao   +3 more
openaire   +3 more sources

Nighttime Dehaze-Enhancement

CoRR, 2022
In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing -- our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting.
Harshan Baskar, Anirudh SSrinivasanChakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout   +9 more
openaire   +3 more sources

Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding [PDF]

, 2023
On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subjective factor, which is to give the user selectable dehazed images rather ...
Gui, Jie, Kwok, James Tin-Yau, Cong, Xiaofeng, He, Lei, Tang, Yuan Yan   +4 more
core   +1 more source