Single Image Haze Removal Using Single Pixel Approach Based on Dark Channel Prior with Fast Filtering

Jo, Sung Yong; Ha, JeongMok; Jeong, Hong

doi:10.1007/978-3-319-46418-3_14

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…. Estimation of A described in original DCP method is used in [21][22][23][24][25][26][27], [10] and many other methods. In this method, A is estimated by first selecting the indices the top 0.1% pixels of dark channel and then using these indices the maximum intensity pixel in the hazy image is selected for A.…”

Section: Flaws In Airlight Estimationmentioning

confidence: 99%

A Novel Image Dehazing and Assessment Method

Sami

Muqeet

Tariq

2019

2019 8th International Conference on Information and Communication Technologies (ICICT)

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Images captured in hazy weather conditions often suffer from color contrast and color fidelity. This degradation is represented by transmission map which represents the amount of attenuation and airlight which represents the color of additive noise. In this paper, we have proposed a method to estimate the transmission map using haze levels instead of airlight color since there are some ambiguities in estimation of airlight. Qualitative and quantitative results of proposed method show competitiveness of the method given. In addition we have proposed two metrics which are based on statistics of natural outdoor images for assessment of haze removal algorithms.

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Section: Flaws In Airlight Estimationmentioning

confidence: 99%

A Novel Image Dehazing and Assessment Method

Sami

Muqeet

Tariq

2019

2019 8th International Conference on Information and Communication Technologies (ICICT)

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“…Combining the DCP with the guided filter [24] is also the most efficient method for the dehazing algorithm. Since then, the principal study of the fog algorithm has focused on the matting technique of transmittance [24][25][26][27][28][29][30]. Meng et al [25] applied a weighted L1-norm-based contextual regularization to optimize the estimation of the unknown scene transmission.…”

Section: Introductionmentioning

confidence: 99%

“…Meng et al [25] applied a weighted L1-norm-based contextual regularization to optimize the estimation of the unknown scene transmission. Sung et al [26] used a fast guided filter that was combined with the up/down samples to optimize the performance time. Zhang et al [28] used the five-dimensional feature vectors to recover the transmission values by finding their nearest neighbors from the fixed points.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

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Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model's design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method.

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Single Image Haze Removal Using Single Pixel Approach Based on Dark Channel Prior with Fast Filtering

Cited by 2 publications

References 15 publications

A Novel Image Dehazing and Assessment Method

A Novel Image Dehazing and Assessment Method

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

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