Single image haze removal based on fusion darkness channel prior

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740037 ◽  
Author(s):  
Xifang Zhu ◽  
Ruxi Xiang ◽  
Feng Wu ◽  
Xiaoyan Jiang
Keyword(s):  
Image Quality ◽  
Channel Model ◽  
Input Image ◽  
The Other ◽  
Fusion Method ◽  
Single Image ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Correlation Information ◽  
Atmospheric Scattering Model

To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed method not only effectively remove the haze of the image, but also outperform the other haze removal methods.

Single image haze removal with approximate radiance darkness prior

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840086 ◽  
Author(s):  
Ruxi Xiang ◽  
Feng Wu
Keyword(s):  
Input Image ◽  
Experimental Results ◽  
Single Image ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Dark Channel ◽  
Atmospheric Scattering Model

In this paper, we propose a novel and effective method for removing haze based on a single image, which firstly computes the dark channel of the estimated radiance image by decomposing the dark channel of the haze input image, and the method then estimates the transmission map of the input image. Finally, the scene radiance image is restored by the classical atmospheric scattering model. Experimental results show that the proposed method outperforms He et al.’s method in terms of haze removal.

Single image haze removal based on two steps

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740038
Author(s):  
Ruxi Xiang ◽  
Xifang Zhu ◽  
Feng Wu
Keyword(s):  
Input Image ◽  
Experimental Results ◽  
Filter Method ◽  
Ambient Light ◽  
Single Image ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Novel Method ◽  
Realistic Scenes ◽  
Atmospheric Scattering Model

In this paper, a novel method named Haze Removal based on Two Steps (HRTS) for removing the haze has been proposed based on two steps, which obviously improves the image qualities such as color and visibility caused by haze. The proposed method mainly consists of two steps: the preprocessing step by decomposing the input image to reduce the influence of ambient light and the removed haze step for restoring the radiance. We first reduce the effect of the ambient light by decomposing the haze image, estimate the transmission map based on the result of the decomposition, and then use the modified guided filter method to refine it. Finally, the monochrome atmospheric scattering model is combined to restore the radiance image. Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image in the realistic scenes by comparing other existing haze removal methods.

scholarly journals Fast Single Image Haze Removal Method based on Atmospheric Scattering Model

2018 ◽  
Vol 51 (17) ◽  
pp. 211-216
Author(s):  
Huang Dewei ◽  
Wang Weixing ◽  
Lu Jianqiang ◽  
Chen Kexin
Keyword(s):  
Single Image ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Removal Method ◽  
Atmospheric Scattering Model

scholarly journals Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Wang ◽  
Wenhui Li ◽  
Qingji Guan ◽  
Miao Qi
Keyword(s):  
State Of The Art ◽  
Local Similarity ◽  
Fusion Method ◽  
Single Image ◽  
Image Dehazing ◽  
Adaptive Wavelet ◽  
Atmospheric Scattering ◽  
Dark Channel ◽  
Computer Vision Applications ◽  
Scene Depth

Removing the haze effects on images or videos is a challenging and meaningful task for image processing and computer vision applications. In this paper, we propose a multiscale fusion method to remove the haze from a single image. Based on the existing dark channel prior and optics theory, two atmospheric veils with different scales are first derived from the hazy image. Then, a novel and adaptive local similarity-based wavelet fusion method is proposed for preserving the significant scene depth property and avoiding blocky artifacts. Finally, the clear haze-free image is restored by solving the atmospheric scattering model. Experimental results demonstrate that the proposed method can yield comparative or even better results than several state-of-the-art methods by subjective and objective evaluations.

Haze Removal for a Single Image Using Adaptive Template Dark Channel Prior

2014 ◽  
Vol 543-547 ◽  
pp. 2480-2483
Author(s):  
Jing Zhang ◽  
Wei Dong ◽  
Juan Li ◽  
Xu Ning Liu
Keyword(s):  
Input Image ◽  
Dark Channel Prior ◽  
Edge Region ◽  
Single Image ◽  
Haze Removal ◽  
Flat Region ◽  
Imaging Model ◽  
Texture Region ◽  
Dark Channel ◽  
Single Input

In this paper, we propose an adaptive template method based on the dark channel prior. The method combines with the haze imaging model to haze removal for a single image. This method can effectively remove haze from a single input image. According to the characteristics of the image itself and the haze removal effect of the different template we divide the input image into flat region, edge region and texture region. Then, select the lager size template dispose the flat region and use midrange or minitype template dispose the edge region and texture area. Experimental results demonstrate that the proposed algorithm has very good performance for fog removal and retains the image details more effectively.

scholarly journals A Bayesian Framework for Single Image Dehazing considering Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Dong Nan ◽  
Du-yan Bi ◽  
Chang Liu ◽  
Shi-ping Ma ◽  
Lin-yuan He
Keyword(s):  
Probability Density Function ◽  
Bayesian Framework ◽  
Iterative Approach ◽  
Single Image ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Degraded Image ◽  
Statistical Prior ◽  
Atmospheric Scattering Model ◽  
Single Image Dehazing

The single image dehazing algorithms in existence can only satisfy the demand for dehazing efficiency, not for denoising. In order to solve the problem, a Bayesian framework for single image dehazing considering noise is proposed. Firstly, the Bayesian framework is transformed to meet the dehazing algorithm. Then, the probability density function of the improved atmospheric scattering model is estimated by using the statistical prior and objective assumption of degraded image. Finally, the reflectance image is achieved by an iterative approach with feedback to reach the balance between dehazing and denoising. Experimental results demonstrate that the proposed method can remove haze and noise simultaneously and effectively.

scholarly journals Image Dehazing Technique Based On DWT Decomposition and Intensity Retinex Algorithm

2019 ◽  
pp. 116-122
Author(s):  
Sunita Shukla ◽  
Silky Pareyani
Keyword(s):  
Median Filter ◽  
Weather Conditions ◽  
Input Image ◽  
Single Image ◽  
Distance Information ◽  
Haze Removal ◽  
Imaging Model ◽  
Single Input ◽  
Adjacent Pixel ◽  
Different Depths

Conventional designs use multiple image or single image to deal with haze removal. The presented paper uses median filer with modified co-efficient (16 adjacent pixel median) and estimate the transmission map and remove haze from a single input image. The median filter prior(co-efficient) is developed based on the idea that the outdoor visibility of images taken under hazy weather conditions seriously reduced when the distance increases. The thickness of the haze can be estimated effectively and a haze-free image can be recovered by adopting the median filter prior and the new haze imaging model. Our method is stable to image local regions containing objects in different depths. Our experiments showed that the proposed method achieved better results than several state-of-the-art methods, and it can be implemented very quickly. Our method due to its fast speed and the good visual effect is suitable for real-time applications. This work confirms that estimating the transmission map using the distance information instead the color information is a crucial point in image enhancement and especially single image haze removal.

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