2020
DOI: 10.1109/access.2020.2981944
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Abstract: 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 generative adversarial networks (CGAN). The hazy image is used to generate the composed image K jointly estimated by a transmission map and atmospheric light value through a generator network, and a dehazed image is calculated through an improved atmospheric sca… Show more

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Cited by 9 publications
(3 citation statements)
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“…The dataset contains 13,990 hazed images, which were generated from clear images of the indoor depth dataset NYU2 [34] and Middlebury [35]. Each clear image generates 10 synthetic hazed images according to the atmospheric scattering model, with global atmospheric light values taking values between 0.7 and 1.0 [37] and atmospheric scattering coefficients chosen uniformly at random between 0.6 and 1.8 [38]. The RESIDE dataset consists of two main components, the indoor training set (ITS) and the outdoor training set (OTS).…”
Section: Methodsmentioning
confidence: 99%
“…The dataset contains 13,990 hazed images, which were generated from clear images of the indoor depth dataset NYU2 [34] and Middlebury [35]. Each clear image generates 10 synthetic hazed images according to the atmospheric scattering model, with global atmospheric light values taking values between 0.7 and 1.0 [37] and atmospheric scattering coefficients chosen uniformly at random between 0.6 and 1.8 [38]. The RESIDE dataset consists of two main components, the indoor training set (ITS) and the outdoor training set (OTS).…”
Section: Methodsmentioning
confidence: 99%
“…With the development f deep machine learning methods, dehazing algorithms [27], [28], [29], [30], [31], and [32] based on deep machine learning methods have also been developed.…”
Section: Introductionmentioning
confidence: 99%
“…By creating a colourtransfer image dehazing model to remove haze obscuration and acquire information regarding the coefficients of the model by using the devised convolutional neural networkbased deep framework as a supervised learning strategy. [27,28] applied the adversarial network to the task of image dehazing. These dehazing methods based on deep learning have obtained promising results; however, the results are easily affected by the dataset.…”
Section: Introductionmentioning
confidence: 99%