Enhancement of Hazy Color Images Using a Self-Tunable Transformation Function

Arigela, Saibabu; Asari, Vijayan K.

doi:10.1007/978-3-319-14364-4_56

Cited by 3 publications

(7 citation statements)

References 9 publications

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“…In order to perform a qualitative and quantitative analysis of the proposed method, we do some simulation experiments on color foggy images in comparison with three pairs of state-of-the-art defogging approaches. The first pair is Ranota and Kaur's [13] and Wang and Ng's [14] that are directly based on histogram equalization technique while the second one is Arigela and Asari's [11] and Liu et al 's [12] which belong to intensity transformation functions. Apparently, it is indispensable for us to choose them as comparative groups, since they are quite relevant to our method.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Nowadays, people change their minds by combining image enhancement algorithms with the physical degradation model. In 2014, Arigela and Asari design a sine nonlinear function to refine a rough scene transmission [11], but the method is more likely to produce results with a dim color and a distorted sky region. This is mainly due to the fact that it relies on precisely estimated scene transmission information.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we propose an improved variational histogram equalization framework for single color image defogging. Similar to the previously presented methods in [11][12][13], histogram equalization and the physical degradation model are merged together into an effective and fast defogging framework in our paper. The major contributions we make can be summarized into four aspects.…”

Section: Introductionmentioning

confidence: 99%

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Variational Histogram Equalization for Single Color Image Defogging

Zhou

2016

Mathematical Problems in Engineering

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Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variational Histogram Equalization for Single Color Image Defogging

Zhou

2016

Mathematical Problems in Engineering

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“…As a result, this prior will not get great results in the large depth regions. There are many articles that are based on the dark channel prior [4]- [7]. Arigela et al [4] designed the sine nonlinear function to enhance the pass from dark channel prior to obtain a rough transmission map in 2014.…”

Section: Introductionmentioning

confidence: 99%

“…There are many articles that are based on the dark channel prior [4]- [7]. Arigela et al [4] designed the sine nonlinear function to enhance the pass from dark channel prior to obtain a rough transmission map in 2014. This method used the gray-scale transformation function to replace the softmatting algorithm in [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Defogging Algorithm Based on the Joint Use of Saturation and Color Attenuation Prior

Du-yan

2018

IEICE Trans. Inf. & Syst.

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Focusing on the defects of famous defogging algorithms for fog images based on the atmosphere scattering model, we find that it is necessary to obtain accurate transmission map that can reflect the real depths both in large depth and close range. And it is hard to tackle this with just one prior because of the differences between the large depth and close range in foggy images. Hence, we propose a novel prior that simplifies the solution of transmission map by transferring coefficient, called saturation prior. Then, under the Random Walk model, we constrain the transferring coefficient with the color attenuation prior that can obtain good transmission map in large depth regions. More importantly, we design a regularization weight to balance the influences of saturation prior and color attenuation prior to the transferring coefficient. Experimental results demonstrate that the proposed defogging method outperforms the state-of-art image defogging methods based on single prior in terms of details restoring and color preserving.

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Multi-scale depth information fusion network for image dehazing

Fan

Hua

2021

Appl Intell

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Enhancement of Hazy Color Images Using a Self-Tunable Transformation Function

Cited by 3 publications

References 9 publications

Variational Histogram Equalization for Single Color Image Defogging

Variational Histogram Equalization for Single Color Image Defogging

Novel Defogging Algorithm Based on the Joint Use of Saturation and Color Attenuation Prior

Multi-scale depth information fusion network for image dehazing

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