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 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.

A Modified Haze Removal Algorithm Using Dark Channel Prior

2012 ◽  
Vol 457-458 ◽  
pp. 1397-1402
Author(s):  
Xiao Tian Wu ◽  
Xing Hao Ding ◽  
Quan Xiao
Keyword(s):  
Adaptive Algorithm ◽  
Bilateral Filter ◽  
Input Image ◽  
Dark Channel Prior ◽  
Edge Preserving ◽  
Haze Removal ◽  
Dark Channel ◽  
Color Shift ◽  
Single Input ◽  
Channel Image

In this paper, we propose a new algorithm to remove haze from a single input image. Based on the Dark Channel Prior proposed by He [1], we exploit the Gauss Bilateral Filter and the min operation to obtain an edge-preserving dark channel image, which is non-iterative, requires less time. We further utilize this dark channel image to extract the estimation of medium transmission, and finally recover a haze-free image from that. Furthermore, we use a self-adaptive algorithm to set the haze parameters to solve the color shift problem for large sky region. Experiments demonstrate our algorithm can effectively remove haze from a foggy image while keep edges sharp.

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.

Accelerated haze removal for a single image by dark channel prior

2019 ◽  
Vol 20 (8) ◽  
pp. 1109-1118 ◽  
Author(s):  
Bo-xuan Yue ◽  
Kang-ling Liu ◽  
Zi-yang Wang ◽  
Jun Liang
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Haze Removal ◽  
Dark Channel

scholarly journals Region Adaptive Single Image Dehazing

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1438
Author(s):  
Changwon Kim
Keyword(s):  
Real Data ◽  
Weather Conditions ◽  
Data Sets ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Texture Region ◽  
Adverse Weather ◽  
Dark Channel ◽  
Single Image Dehazing

Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.

Saliency-based dark channel prior model for single image haze removal

2018 ◽  
Vol 12 (6) ◽  
pp. 1049-1055 ◽  
Author(s):  
Libao Zhang ◽  
Shiyi Wang ◽  
Xiaohan Wang
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Prior Model ◽  
Haze Removal ◽  
Dark Channel
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