scholarly journals Haze Removal System using Dark Channel Prior

2021 ◽  
Vol 9 (VI) ◽  
pp. 301-309
Author(s):  
Disha M. Jaiswal
Keyword(s):  
Road Traffic ◽  
Winter Season ◽  
Road Transport ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Haze Removal ◽  
The Road ◽  
Dark Channel ◽  
Atmospheric Light ◽  
Removal System

Mostly in winter season, the Northern area of India is mostly affected due to heavy haze. The road traffic and air traffic is affected due to poor visibility. According to the survey of Ministry of Road Transport and Highways of India, the number of accident due to poor visibility increasing every year. Hence there is need of robust algorithm to enhance the visibility of the camera feed. In the proposed approach, image dehazing algorithm has been present using dark channel prior. The proposed algorithm is developed for outdoor images. The proposed system processed the image through dark channel prior, estimation of atmospheric light, estimation of transmission and scene radiance. The proposed system achieved the promising results on O-Haze dataset.

scholarly journals Restoration and Enhancement of Underwater Images Based on Bright Channel Prior

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yakun Gao ◽  
Haibin Li ◽  
Shuhuan Wen
Keyword(s):  
Histogram Equalization ◽  
New Method ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Underwater Environment ◽  
Dark Channel ◽  
Bright Channel Prior ◽  
Channel Image ◽  
Atmospheric Light

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

Pipeline image haze removal system using dark channel prior on cloud processing platform

2020 ◽  
Vol 22 (1) ◽  
pp. 84
Author(s):  
Ruili Liu ◽  
Jing Zheng ◽  
Liguo Zhang ◽  
Tan He ◽  
Yingheng Wang ◽  
...  
Keyword(s):  
Dark Channel Prior ◽  
Haze Removal ◽  
Cloud Processing ◽  
Dark Channel ◽  
Processing Platform ◽  
Removal System

scholarly journals Image Dehazing Based on Physical Model and Gray Projection

2019 ◽  
Vol 9 (1S3) ◽  
pp. 241-245
Keyword(s):  
Image Restoration ◽  
Physical Model ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Dark Channel ◽  
Restoration Techniques ◽  
Image Computation ◽  
Computing Speed ◽  
Atmospheric Light

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.

Single-Image Dehazing via Dark Channel Prior and Adaptive Threshold

2021 ◽  
pp. 2150053
Author(s):  
Yongpeng Pan ◽  
Zhenxue Chen ◽  
Xianming Li ◽  
Weikai He
Keyword(s):  
Adaptive Threshold ◽  
Dark Channel Prior ◽  
Segmentation Method ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Traditional Algorithm ◽  
Computer Vision Systems ◽  
Atmospheric Light ◽  
Single Image Dehazing

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm.

scholarly journals Real-Time Haze Removal Using Normalised Pixel-Wise Dark-Channel Prior and Robust Atmospheric-Light Estimation

2020 ◽  
Vol 10 (3) ◽  
pp. 1165 ◽  
Author(s):  
Yutaro Iwamoto ◽  
Naoaki Hashimoto ◽  
Yen-Wei Chen
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Dark Channel Prior ◽  
Detailed Structure ◽  
Haze Removal ◽  
Acceptable Quality ◽  
Colour Channel ◽  
Dark Channel ◽  
High Computational Cost ◽  
Atmospheric Light

This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the spatial resolution of the transmission map depends on the patch size and it loses the detailed structure with large patch sizes, original work refines the transmission map using an image-matting technique. However, it requires high computational cost and is not adequate for real-time application. To solve these problems, we use normalised pixel-wise haze estimation without losing the detailed structure of the transmission map. This study also proposes robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation for acceleration. Experiments with actual and simulated haze images showed that the proposed method achieves real-time results of visually and quantitatively acceptable quality compared with other conventional methods of haze removal.

scholarly journals Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods

2019 ◽  
Vol 8 (12) ◽  
pp. 3015-3020
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Ground Truth ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Blocking Artifacts ◽  
Image Dehazing ◽  
Data Set ◽  
Dark Channel ◽  
Atmospheric Light

Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.

Enhancement of Hazy Images Using Atmospheric Light Estimation Technique

2020 ◽  
pp. 2150078
Author(s):  
Kalimuddin Mondal ◽  
Rinku Rabidas ◽  
Rajdeep Dasgupta ◽  
Abhishek Midya ◽  
Jayasree Chakraborty
Keyword(s):  
Efficient Method ◽  
Computational Efficiency ◽  
Weather Conditions ◽  
Dark Channel Prior ◽  
Estimation Technique ◽  
Single Image ◽  
Retaining Structure ◽  
Haze Removal ◽  
Dark Channel ◽  
Atmospheric Light

Images captured in degraded weather conditions often suffer from bad visibility. Pre-existing haze removal methods, the ones that are effective are computationally complex too. In common de-hazing approaches, estimation of atmospheric light is not achieved properly as a consequence, haze is not removed significantly from the sky region. In this paper, an efficient method of haze removal from a single image is introduced. To restore haze-free images comprising of both sky as well as nonsky regions, we developed a linear model to predict atmospheric light and estimated the transmission map using the dark channel prior followed by an application of a guided filter for quick refinement. Several experiments were conducted on a large variety of images, both reference and nonreference, where the proposed image de-hazing algorithm outperforms most of the prevalent algorithms in terms of perceptual visibility of the scene and computational efficiency. The proposed method has been empirically measured through quantitative and qualitative evaluations while retaining structure, edges, and improved color.

Single Image Haze Removal Based on Dark Channel Prior Applied on Air Duct Robot

2012 ◽  
Vol 220-223 ◽  
pp. 1307-1310
Author(s):  
Peng Fei Yang ◽  
Wei Sun ◽  
Sheng Nan Liu ◽  
Ming Hua Ouyang
Keyword(s):  
Experimental Results ◽  
Good Effect ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Haze Removal ◽  
Dark Channel ◽  
Cleaning Robots ◽  
Duct Cleaning

The rule of dark channel prior has made significant effect in outdoor image dehazing. The camera on air duct cleaning robots will capture foggy pictures when they are working because of raised dust, these foggy pictures have serious impact on the robot cleaning work. According to the characteristics of pictures in air ducts, we remove haze of these images based on dark channel prior, experimental results show that this method has good effect.

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