VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

Ju, Mingye; Chen, Chuheng; Liu, Juping; Cheng, Kai

doi:10.1109/cvprw53098.2021.00100

Cited by 3 publications

(3 citation statements)

References 56 publications

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“…Hazy images often suffer from colour cast. To achieve high colour fidelity, numerous studies have found that an accurate estimation of airlight can prevent colour cast [32,[39][40][41]. These airlight estimation methods can be classified into three categories.…”

Section: Airlight Estimationmentioning

confidence: 99%

“…The second category attempts to identify areas of sky in order to estimate the airlight. They employ various strategies, such as analyzing the upper portion of the image or averaging large intensities, to ensure a robust estimation [41]. However, these two categories of methods have limitations in their applications, especially when the sky areas are not visible in the image, or when the largest portion of intensities cannot accurately estimate the airlight.…”

Section: Airlight Estimationmentioning

confidence: 99%

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Single image dehazing via decomposition and enhancement

Gu,

Yao,

Sun

et al. 2023

IET Image Processing

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Hazy images suffer from two problems. The low contrast can be enhanced by estimating a transmission layer, and the colour cast can be restored by estimating an airlight. These two variables, together with the albedo layer, are the constitutive elements of a hazy image. The resulting quality of dehazed images is inextricably linked to the accurate estimation of these components. However, it is ill‐posed to decompose these variables from a single image. As such, this paper presents an innovative algorithm intended to facilitate the optimal decomposition of a hazy image. Using the Markov random field model, an optimal framework is established that allows the simultaneous estimation of the three components across the three‐colour channels. To improve the visual quality, three improvements are proposed in the variational solution for the optimal components. The dehazed result is recomposed from the components with the transmission enhanced to circumvent any potential artefacts or information loss. Extensive experiments on natural images corroborate that the proposed algorithm outperforms state‐of‐the‐art dehazing methods, both qualitatively and quantitatively.

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Section: Airlight Estimationmentioning

confidence: 99%

Section: Airlight Estimationmentioning

confidence: 99%

Single image dehazing via decomposition and enhancement

Gu,

Yao,

Sun

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Jiang et al [25] employed a proxy-based method to learn a refined optical depth PRG model and selected features related to fog density, such as the dark channel, saturation value, and chrominance, for fog density estimation and image defogging. Ju et al [26] defined a fog density index model to guide image defogging. This model utilizes the positive correlation between fog density's minimum and range values, and the dark channel.…”

Section: Fog Density Estimationmentioning

confidence: 99%

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Cao

Wang

2023

Atmosphere

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The fog density level, as one of the indicators of weather conditions, will affect the management decisions of transportation management agencies. This paper proposes an image-based method to estimate fog density levels to improve the accuracy and efficiency of analyzing fine meteorological conditions and validating fog density predictions. The method involves two types of image entropy: a two-dimensional directional entropy derived from four-direction Sobel operators, and a combined entropy that integrates the image directional entropy and grayscale entropy. For evaluating the performance of the proposed method, an image test set and an image training set are constructed; and each image is labeled as heavy fog, moderate fog, light fog, or fog-free according to the fog density level of the image based on a user study. Using our method, the average accuracy rates of image fog level estimation were 77.27% and 79.39% on the training set using the five-fold cross-validation and the test set, respectively. Our experimental results demonstrate the effectiveness of the proposed combined entropy for image-based fog density level estimation.

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Layer Separation Network with Contrastive Loss for Robust Dehazing

Wang

Guo

et al. 2023

2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

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VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

Cited by 3 publications

References 56 publications

Single image dehazing via decomposition and enhancement

Single image dehazing via decomposition and enhancement

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Layer Separation Network with Contrastive Loss for Robust Dehazing

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