Image Defogging Algorithm Based on Sky Region Segmentation and Dark Channel Prior

2020 ◽  
Vol 8 (5) ◽  
pp. 476-486
Author(s):  
Zuyun Jiang ◽  
Xiangdong Sun ◽  
Xiaochun Wang
Keyword(s):  
Time Complexity ◽  
Quantitative Measurement ◽  
Color Space ◽  
Dark Channel Prior ◽  
Region Segmentation ◽  
Low Frequencies ◽  
Image Defogging ◽  
Dark Channel ◽  
Hsi Color Space ◽  
Initial Transmission

AbstractBased on image segmentation and the dark channel prior, this paper proposes a fog removal algorithm in the HSI color space. Usually, the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area, because the sky region does not meet the prior assumption. For this reason, our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I. Based on the segmentation result, the initial transmission map is modified in turn, and finally refined by the guided filter. The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise, and stretched by multiplying a constant related to the initial transmission map. Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.

scholarly journals Improved Dark Channel Prior for Image Defogging Using RGB and YCbCr Color Space

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 32576-32587 ◽  
Author(s):  
Zahid Tufail ◽  
Khawar Khurshid ◽  
Ahmad Salman ◽  
Imran Fareed Nizami ◽  
Khurram Khurshid ◽  
...  
Keyword(s):  
Color Space ◽  
Dark Channel Prior ◽  
Ycbcr Color Space ◽  
Image Defogging ◽  
Dark Channel

scholarly journals Variational Histogram Equalization for Single Color Image Defogging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Li Zhou ◽  
Du Yan Bi ◽  
Lin Yuan He
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Color Space ◽  
Histogram Equalization ◽  
Degradation Model ◽  
Image Defogging ◽  
Hsi Color Space ◽  
General Variation ◽  
Bad Weather ◽  
Physical Degradation

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.

An improved dark channel prior image defogging algorithm based on wavelength compensation

2019 ◽  
Vol 12 (4) ◽  
pp. 501-512
Author(s):  
Zhixiang Chen ◽  
Binna Ou ◽  
Qianyi Tian
Keyword(s):  
Dark Channel Prior ◽  
Image Defogging ◽  
Dark Channel

Fog Removal Algorithms for Real-Time Video Footage in Smart Cities for Safe Driving

2019 ◽  
pp. 55-86
Author(s):  
Neetu Sood ◽  
Indu Saini ◽  
Tarannum Awasthi ◽  
Milin Kaur Saini ◽  
Parul Bhoriwal ◽  
...  
Keyword(s):  
Time Complexity ◽  
Smart Cities ◽  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Safe Driving ◽  
Video Footage ◽  
Video Frames ◽  
Dark Channel ◽  
Retinex Algorithm ◽  
Before And After

In this chapter, different approaches are presented for removal of fog from video footage taken in moving cars. The methodology uses different approaches, namely dark channel prior, contrast limited adaptive histogram equalization (CLAHE), the combination of two approaches (dark channel prior and CLAHE), and RETINEX algorithm combined with DWT. The algorithms are implemented in MATLAB R2015a. Moreover, the algorithms are compared based on their computational complexity and a visibility metric which is used for computing the CNR of video frames before and after the application of the algorithm. The chapter discusses which algorithm would provide better performance during night fog and daylight fog. Finally, the safe speed of the driver is calculated based on the time complexity of the algorithm used.

Single Image Dehazing with V-transform and Dark Channel Prior

2020 ◽  
Vol 8 (2) ◽  
pp. 185-194
Author(s):  
Xiaochun Wang ◽  
Xiangdong Sun ◽  
Ruixia Song
Keyword(s):  
Median Filter ◽  
Color Space ◽  
Low Frequency ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Color Distortion ◽  
Dark Channel ◽  
Reconstruction Image ◽  
Single Image Dehazing

AbstractSingle image dehazing algorithm based on the dark channel prior may cause block effect and color distortion. To improve these limitations, this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior, in which a hazy RGB image is converted into the HSI color space, and each component H, I and S is processed separately. The hue component H remains unchanged, the saturation component S is stretched after being denoised by a median filter. In the procession of intensity component, a quad-tree algorithm is presented to estimate the atmospheric light, the dark channel prior and the V-transform are used to estimate the transmission map. To reduce the computational complexity, the intensity component I is decomposed by the V-transform first, coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image, and the guided filter is finally employed to refine the coarse transmission map. For images with sky regions, the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map. The proposed algorithm has low time complexity and performs well on a wide variety of images. The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.

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