Single Image Haze Removal Using White Balancing and Saliency Map

Ansia, S.; Aswathy, A. L.

doi:10.1016/j.procs.2015.01.042

Cited by 13 publications

(6 citation statements)

References 9 publications

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“…Anisa proposed use of white balancing which eliminates the color cast of atmospheric color and applied CLAHE, which formed less spurious edges and restore the color as in natural scene. On the basis of comparative result average PSNR of RMSHE, RSIHE, CLAHE, AGCWD, AGCPF on more than 25 images, it can be identified that CLAHE is having the maximum PSNR [10]. It is known that higher the PSNR value, higher the image quality and hence better visibility of the image objects.…”

Section: Aoptical Model Of Scattered Image Formation In Steady Weathermentioning

confidence: 96%

“…It is difficult to process video frames in RGB color model so Yoon suggested and implemented HSV color space model. He modeled S channel for atmospheric light and V channel for image intensity [10]. In this paper Yoon enhanced the visibility of foggy video frames by a combination of atmospheric light and transmission map with consideration of temporal correlation of consecutive frames.…”

Section: Aoptical Model Of Scattered Image Formation In Steady Weathermentioning

confidence: 99%

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Enhancing Visibility Range in Foggy Weather by Contrast Stretched CLAHE (CS- CLAHE)

Jain¹,

Nirvikar²

2019

IJITEE

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Road accidents are becoming a very common problem due to unfavorable weather conditions like rain, fog, and pollution etc. The reason behind such accident is due to poor visibility outside, on the roads and hence driving becomes a problematic issue. Poor visibility becomes due to atmospheric trend originated such as fog, mist originated by small particles in the air such as dust, smoke or moisture, which reduces the visibility of the outdoor scenes. In this paper we are suggesting a Contrast Stretched CLAHE (CS-CLAHE) which will enhance the visibility of outside environment to take necessary precautions for prevention and avoiding the accidents. In the situation of real time streaming, there is a sequence of multiple frames per second, which are to be processed for enhancing the streaming. Therefore, this paper will discuss as a first step: how to enhance a single image and at second stage, processing of multiple images of streaming in parallel processing environment. For fast processing FPGA model is suggested.

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Section: Aoptical Model Of Scattered Image Formation In Steady Weathermentioning

confidence: 96%

Section: Aoptical Model Of Scattered Image Formation In Steady Weathermentioning

confidence: 99%

Enhancing Visibility Range in Foggy Weather by Contrast Stretched CLAHE (CS- CLAHE)

Jain¹,

Nirvikar²

2019

IJITEE

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“…We developed a linear regression model to estimate the haze concentration based on investigating various haze relevant features. Saliency represents which regions stand out from the neighbors and are the most attractive [26]. Due to the influence of haze, a large amount of information (color, edge, etc.)…”

Section: Transmission Estimationmentioning

confidence: 99%

“…in the image is damaged, and the saliency of the target is also greatly affected. So the luminance component I , the saliency component S , and the saturation component I have a strong relationship with the distribution of haze [15,26,27]. The concentration of haze F(x, y) is described as follows:…”

Section: Transmission Estimationmentioning

confidence: 99%

Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Shao

Guo

Zhang

et al. 2019

Mathematical Problems in Engineering

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Given that a single remote sensing image dehazing is an ill-posed problem, this is still a challenging task. In order to improve the visibility of a single hazy remote sensing multispectral image, we developed a novel and effective algorithm based on a learning framework. A linear regression model with the relevant features of haze was established. And the gradient descent method is applied to the learning model. Then a hazy image accurate transmission map is obtained by learning the coefficients of the linear model. In addition, we proposed a more effective method to estimate the atmospheric light, which can restrain the influence of highlight areas on the atmospheric light acquisition. Compared with the traditional haze removal methods, the experimental results demonstrate that the proposed algorithm can achieve better visual effect and color fidelity. Both subjective evaluation and objective assessments indicate that the proposed method achieves a better performance than the state-of-the-art methods.

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“…This algorithm could add the depth of field information to make the image more realistic. Many improvements have been made to the method [13][14][15][16][17][18][19] . Hu et al [20] proposed an image dehazing algorithm based on an atmosphere scattering approximation model; Ding et al [21] proposed an effective DCP algorithm based on image dehazing using quad trees; Wang et al [22] proposed an improved single image dehazing algorithm using DCP; Wang et al [23] used a physical model to explain the objective rationality of the DCP method.…”

Section: Introductionmentioning

confidence: 99%

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Wang

Yang

Zhang

et al. 2018

International Journal of Agricultural and Biological Engineering

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Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring.

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Single Image Haze Removal Using White Balancing and Saliency Map

Cited by 13 publications

References 9 publications

Enhancing Visibility Range in Foggy Weather by Contrast Stretched CLAHE (CS- CLAHE)

Enhancing Visibility Range in Foggy Weather by Contrast Stretched CLAHE (CS- CLAHE)

Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

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