Results 21 to 30 of about 5,362 (169)

Survey of Transformer-Based Single Image Dehazing Methods [PDF]

Jisuanji kexue yu tansuo
As a fundamental computer vision task, image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality, thereby the clear images can be recovered for subsequent high-level ...
ZHANG Kaili, WANG Anzhi, XIONG Yawei, LIU Yun
doaj   +1 more source

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   +1 more source

Unsupervised water scene dehazing network using multiple scattering model.

PLoS ONE, 2021
In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be.
Shunmin An, Xixia Huang, Linling Wang, Zhangjing Zheng, Le Wang   +4 more
doaj   +1 more source

Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation

Applied Sciences, 2023
The precision in evaluating global ambient light profoundly impacts the performance of image-dehazing technologies. Many approaches for quantifying atmospheric light intensity suffer from inaccuracies, leading to a decrease in dehazing effectiveness.
Shuai Liu, Ying Li, Hang Li, Bin Wang, Yuanhao Wu, Zhenduo Zhang   +5 more
doaj   +1 more source

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   +1 more source

NTIRE 2020 Challenge on NonHomogeneous Dehazing

, 2020
This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze ...
Ancuti, Codruta O., Ancuti, Cosmin, Chang, Jerome, Chang, Yu-Bang, Chao, Ju-Chin, Chen, Peng-Wen, Chen, Siyi, Cherukuri, Venkateswararao, Chuang, Shiue-Yuan, Das, Sourya Dipta, de Dravo, Vincent Whannou, Deng, Qili, Do, Jeonghyeok, Dong, Yu, Dutta, Saikat, Estrada, Dennis, Guo, Tiantong, Hsu, Po-Min, Huang, Ziling, Kim, Munchurl, Kong, Xiangzhen, Lee, David, Li, Xuelu, Liao, Tzu-Yi, Lin, Chang-Hong, Lin, Tsung-Nan, Liu, Jing, Ma, Lizhuang, Mandal, Murari, Mehra, Aryan, Metwaly, Kareem, Monga, Vishal, Narang, Pratik, Ouyang, Bing, Park, Jongmin, Qu, Yanyun, Raghunath, Navaneeth, Su, Tianqi, Sun, Bangyong, Sun, Chung-En, Timofte, Radu, Vasluianu, Florin-Alexandru, Wang, Meiqi, Wang, Zhan-Han, Wu, Haiyan, Wu, Pei-Yuan, Xie, Yuan, Yang, Tsung-Shan, Yu, Mingzhao, Yu, Zhe, Zhao, Xuan, Zhou, Hongyu   +51 more
core   +1 more source

Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning

Remote Sensing, 2023
Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments.
Jianchong Wei, Yan Cao, Kunping Yang, Liang Chen, Yi Wu   +4 more
doaj   +1 more source

Remote Sensing Image Dehazing Based on an Attention Convolutional Neural Network

IEEE Access, 2022
Haze may affect the quality of optical remote sensing images, thus limiting the scope of their application. Remote sensing image dehazing has become important in remote sensing image preprocessing, promoting the use of remote sensing data and the ...
Zhijie He, Cailan Gong, Yong Hu, Lan Li
doaj   +1 more source

Multi-level perception fusion dehazing network.

PLoS ONE, 2023
Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and
Xiaohua Wu, Zenglu Li, Xiaoyu Guo, Songyang Xiang, Yao Zhang   +4 more
doaj   +1 more source

Fusion-based Variational Image Dehazing [PDF]

IEEE Signal Processing Letters, 2016
We propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that ...
Adrian Galdran, Javier Vazquez-Corral, David Pardo, Marcelo Bertalmio   +3 more
openaire   +3 more sources