Results 31 to 40 of about 11,754,470 (203)
Aerial Image Dehazing Using Reinforcement Learning
, 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 +3 more
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Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks
IEEE Access, 2020 In recent years, the hazy weather in China occurs frequently, and image dehazing has gradually become a research hotspot. To improve the dehazing effect of the hazy images, this paper has proposed a multilevel image dehazing algorithm using conditional ...
Kailei Gan, Jieyu Zhao, Hao Chen
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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 +4 more
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Multiscale Image Dehazing Method Combining Sky Segmentation and Pyramid Fusion [PDF]
Jisuanji gongcheng, 2023 A multiscale image dehazing method that combines sky segmentation and pyramid fusion is proposed to address contrast degradation, dark tones, and overexposure in traditional image dehazing methods.
Xinjie XIAO, Zhiwei LI, Nannan ZHANG, Yuqing SUN, Wuneng ZHOU
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Journal of Advanced Transportation, 2021 Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version.
Xianjun Hu +3 more
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Single image dehazing based on hazy features extraction and enhancement network
Journal of Measurement Science and Instrumentation, 2023 Convolutional neural network is developing rapidly in image processing. Most image dehazing algorithms only focus on dehazing but neglect the overall quality of dehazing image, which leads to problems such as loss of information blurred texture, etc.
ZHANG Jinlong, YANG Yan
doaj
Applied Sciences, 2022 Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study
Yuanbo Yang +5 more
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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 +3 more
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Advances in Single-Image Dehazing via Unsupervised Learning [PDF]
Jisuanji kexue yu tansuoImage dehazing is an important research direction in image restoration for computer vision. Publicly available datasets are mainly dominated by large-scale synthetic hazy image datasets, while real-world hazy image datasets remain relatively limited.
ZHANG Mengmeng, YANG Ying, ZHAO Jia, WANG Huiling
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A Model-Driven Deep Dehazing Approach by Learning Deep Priors
IEEE Access, 2021 Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks.
Dong Yang, Jian Sun
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