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AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype. [PDF]

Sci Rep
Ding JT, Peng YY, Huang M, Zhou SJ.
europepmc   +1 more source

UR2P-Dehaze: Learning a Simple Image Dehaze Enhancer via Unpaired Rich Physical Prior

Pattern Recognition
Image dehazing techniques aim to enhance contrast and restore details, which are essential for preserving visual information and improving image processing accuracy.
Shivakumara Palaiahnakote, Minglong Xue, Mingliang Zhou   +2 more
exaly   +8 more sources

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Dehaze-AGGAN: Unpaired Remote Sensing Image Dehazing Using Enhanced Attention-Guide Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing, 2022
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 adversarial network (GAN) network, Dehaze-AGGAN, to solve the remote
Tao Mingliang, Jia Su, Shun Zhang
exaly   +2 more sources

Dehaze of Cataractous Retinal Images Using an Unpaired Generative Adversarial Network

IEEE Journal of Biomedical and Health Informatics, 2020
Cataracts are the leading cause of visual impairment worldwide. Examination of the retina through cataracts using a fundus camera is challenging and error-prone due to degraded image quality.
Mingzhai Sun, Lei Liu, Jicheng Liu
exaly   +2 more sources

A Self-Adaption Single Image Dehaze Method Based on Clarity-Evaluation-Function of Image

International Conference on Advanced Mechatronic Systems, 2018
The quality of image collected under severe weather such as fog and haze is badly damaged due to the atmospheric scattering. In order to solve the problem that image dehazing algorithms have poor adaptability, which will occur contrast distortion after ...
Wenqiang Zhang, Yitao Liang
exaly   +2 more sources

Binocular Image Dehazing via a Plain Network Without Disparity Estimation [PDF]

IEEE transactions on multimedia
Heavy haze leads to severely degraded visual quality for images, and thus the performance of high level image-based tasks such as object detection is deteriorated.
Jing Nie, Yan-Wei Pang, J. Xie, Jungong Han, Xue-Long Li   +4 more
semanticscholar   +2 more sources

Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing

Lecture Notes in Computer Science, 2018
Dong Yang, Jian Sun
exaly   +2 more sources

Single Image Dehazing Using End-to-End Deep-Dehaze Network

Electronics, 2021
Haze is a natural distortion to the real-life images due to the specific weather conditions. This distortion limits the perceptual fidelity, as well as information integrity, of a given image.
Masud An Nur Islam Fahim, Ho Yub Jung
exaly   +2 more sources

Depth aware image dehazing

The Visual Computer, 2021
Image dehazing aims to remove the haze noise and restore the image content from hazy images. It is a challenging task because of the unbalanced distribution of the haze noise and the variety of the image contents. Most existing methods apply convolutional neural networks to learn the dehazing process by blind end-to-end training, which relies on the ...
Fei Yang, Qian Zhang
openaire   +1 more source

An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection

Comput. Animat. Virtual Worlds, 2023
Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production.
S. Shit, D. Das, D. Ray, B. Roy
semanticscholar   +1 more source