Výsledky vyhľadávania - Image Dehazing Encoder and Decoder Network Generative Adversarial Network Multi

LiteDHAZE: An Adversarial Dehazing Network for Robust Robotic Perception in Challenging Visual Conditions.

Autori: Zhang, Hanxiang Cherfouh, Koceila Liu, Wei a ďalší

Zdroj: Procedia Computer Science. 2025, Vol. 271, p190-196. 7p.

Predmety: Haze, Generative adversarial networks, Image enhancement (Imaging systems), Image quality analysis, Cognitive robotics, Embedded computer systems, Real-time computing

ED-Dehaze Net: Encoder and Decoder Dehaze Network

Autori: Zhang, Hongqi Wei, Yixiong Zhou, Hongqiao a ďalší

Zdroj: Re-Unir. Archivo Institucional de la Universidad Internacional de La Rioja
Universidad Internacional de La Rioja (UNIR)
instname
International Journal of Interactive Multimedia and Artificial Intelligence, Vol 7, Iss 5, Pp 93-99 (2022)

Predmety: Technology, image dehazing, encoder and decoder network, generative adversarial network, 0202 electrical engineering, electronic engineering, information engineering, IJIMAI, multi-scale convolution block, 02 engineering and technology, generative adversarial etwork, 01 natural sciences, loss function, 0104 chemical sciences

Prístupová URL adresa: https://reunir.unir.net/handle/123456789/13712
https://doi.org/10.9781/ijimai.2022.08.008
https://doaj.org/article/53b711c170f14f7fb349d0e399884919
https://reunir.unir.net/handle/123456789/13712

An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing.

Autori: Zhao, Liquan Zhang, Yupeng Cui, Ying

Zdroj: IEEE Sensors Journal; Jun2022, Vol. 22 Issue 11, p10890-10900, 11p

Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network.

Autori: Zhao, Liquan Yin, Yanjiang Zhong, Tie a ďalší

Zdroj: Sensors (14248220). Sep2023, Vol. 23 Issue 17, p7484. 21p.

Predmety: *GENERATIVE adversarial networks, *REMOTE sensing, *SIGNAL-to-noise ratio

UTCR-Dehaze: U-Net and transformer-based cycle-consistent generative adversarial network for unpaired remote sensing image dehazing.

Autori: Li, Canlin Zhang, Xiangfei Wang, Hua a ďalší

Zdroj: Engineering Applications of Artificial Intelligence. Oct2025:Part B, Vol. 158, pN.PAG-N.PAG. 1p.

Predmety: *REMOTE sensing, *GENERATIVE adversarial networks, *APPLIED sciences, *TRANSFORMER models, *DEEP learning, *IMAGE quality in imaging systems, *IMAGE reconstruction

CGGAN: a context‐guided generative adversarial network for single image dehazing

Autori: Zhenghao Shi Zhaorun Zhou

Zdroj: IET Image Processing, Vol 14, Iss 15, Pp 3982-3988 (2020)

Predmety: image haze removal, single image dehazing, context‐extraction net, QA76.75-76.765, context‐guided generative adversarial network, Photography, 0202 electrical engineering, electronic engineering, information engineering, CGGAN, fusion net, Computer software, 02 engineering and technology, TR1-1050

Prístupová URL adresa: http://arxiv.org/pdf/2005.13884
https://doaj.org/article/8bac0400c8ea480eaa8d3082c94c7168
http://dblp.uni-trier.de/db/journals/iet-ipr/iet-ipr14.html#ZhouS20
https://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-13884
https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-ipr.2020.1153
https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.1153
https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2020.1153

GAN-based E-D Network to Dehaze Satellite Images.

Alternate Title: Red E-D basada en GAN para retocar imágenes de satélite. (Spanish)

Autori: Mallesh, Sudhamalla Haripriya, D.

Zdroj: Data & Metadata; 2024, Vol. 3, p1-11, 11p

Predmety: GENERATIVE adversarial networks, REMOTE-sensing images, REMOTE sensing, HAZE, ALGORITHMS

CGGAN: a context-guided generative adversarial network for single image dehazing.

Autori: Zhou, Zhaorun Shi, Zhenghao

Zdroj: IET Image Processing (Wiley-Blackwell); 2020, Vol. 14 Issue 15, p3982-3988, 7p

Predmety: GENERATIVE adversarial networks, IMAGE reconstruction, IMAGE denoising, IMAGE fusion

Self-supervised polarization image dehazing method via frequency domain generative adversarial networks.

Autori: Sun, Rui Chen, Long Liao, Tanbin a ďalší

Zdroj: Pattern Recognition. Sep2025, Vol. 165, pN.PAG-N.PAG. 1p.

Predmety: *GENERATIVE adversarial networks, *TRAFFIC monitoring, *STOKES parameters, *FREQUENCY discriminators, *ATMOSPHERIC models, *PROBABILISTIC generative models

A Novel Approach to Maritime Image Dehazing Based on a Large Kernel Encoder–Decoder Network with Multihead Pyramids.

Autori: Yang, Wei Gao, Hongwei Jiang, Yueqiu a ďalší

Zdroj: Electronics (2079-9292); Oct2022, Vol. 11 Issue 20, p3351-N.PAG, 16p

Predmety: SEA stories, GENERATIVE adversarial networks, PYRAMIDS, DIGITAL twin, CONVOLUTIONAL neural networks, REMOTELY piloted vehicles

A novel multiscale cGAN approach for enhanced salient object detection in single haze images.

Autori: Dhara, Gayathri Kumar, Ravi Kant

Zdroj: EURASIP Journal on Image & Video Processing; 9/15/2024, Vol. 2024 Issue 1, p1-22, 22p

Predmety: OBJECT recognition (Computer vision), GENERATIVE adversarial networks, COMPUTER vision, WEATHER, SIGNAL-to-noise ratio

ED-Dehaze Net: Encoder and Decoder Dehaze Network

Autori: Zhang, Hongqiang Wei, Yixiong Zhou, Hongqiao a ďalší

Zdroj: IJIMAI, ISSN 1989-1660, Vol. 7, Nº. 5, 2022, pags. 93-99

Predmety: Image Dehazing Encoder and Decoder Network Generative Adversarial Network Multi-Scale Convolution Block Loss Function

Popis súboru: application/pdf

Relation: https://dialnet.unirioja.es/servlet/oaiart?codigo=9034973; (Revista) ISSN 1989-1660

Dostupnosť: https://dialnet.unirioja.es/servlet/oaiart?codigo=9034973

Delving Deeper Into Image Dehazing: A Survey

Autori: Guohou Li Jia Li Gongchao Chen a ďalší

Zdroj: IEEE Access, Vol 11, Pp 131759-131774 (2023)

Predmety: Image dehazing, deep learning, convolutional neural networks (CNNs), generative adversarial networks (GANs), Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis súboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10325493/; https://doaj.org/toc/2169-3536

Prístupová URL adresa: https://doaj.org/article/ad57431a48af480c8289dcf5815ad693

Underwater image restoration using physical models with generative adversarial network and unified transformer framework.

Autori: Zhang, Zhongmin Zhao, Haisheng Li, Zexu

Zdroj: Journal of Electronic Imaging; Nov/Dec2024, Vol. 33 Issue 6, p63018-63018-16, 1p

Predmety: GENERATIVE adversarial networks, SUPERVISED learning, IMAGE reconstruction, PARAMETER estimation, FILTER banks

Image dehazing method via a cycle generative adversarial network.

Autori: Zhao, Chao-Yue Jia, Rui-Sheng Liu, Qing-Ming a ďalší

Zdroj: IET Image Processing (Wiley-Blackwell); 2020, Vol. 14 Issue 16, p4240-4247, 8p

Predmety: GENERATIVE adversarial networks, PROBABILISTIC generative models, SIGNAL-to-noise ratio, IMAGE reconstruction

Densely Connected Pyramid Dehazing Network

Autori: Zhang, He Patel, Vishal M.

Témy: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, text

URL: http://arxiv.org/abs/1803.08396

Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images

Autori: Ding, Xueyan Wang, Yafei Yan, Yang a ďalší

Témy: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/1907.05595

Dehazing algorithm for unmanned surface vessel based on GAN-U-Net++ network.

Autori: Wei, Xing Wang, Bo Li, Ye a ďalší

Zdroj: Journal of Electronic Imaging; Jul2022, Vol. 31 Issue 4, p43014-16, 1p

Predmety: GENERATIVE adversarial networks, SIGNAL-to-noise ratio

Multi‐scale feature aggregation network for single‐image dehazing.

Autori: Zhao, Donghui Mo, Bo Zhu, Xiang

Zdroj: IET Image Processing (Wiley-Blackwell); 9/18/2024, Vol. 18 Issue 11, p2943-2961, 19p

Predmety: CONVOLUTIONAL neural networks, TRANSFORMER models, COMPUTER vision, IMAGE intensifiers, FEATURE extraction

UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder–Decoder.

Autori: Zhao, Anxin Li, Liang Liu, Shuai

Zdroj: Journal of Imaging; Jul2024, Vol. 10 Issue 7, p164, 15p