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.

scholarly journals A Fast Single-Image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 73330-73339 ◽  
Author(s):  
Jehoiada Jackson ◽  
She Kun ◽  
Kwame Obour Agyekum ◽  
Ariyo Oluwasanmi ◽  
Parinya Suwansrikham
Keyword(s):  
Rayleigh Scattering ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

scholarly journals An image dehazing algorithm based on double priors constraint

2018 ◽  
Vol 189 ◽  
pp. 04009
Author(s):  
Kun Liu ◽  
Shiping Wang ◽  
Linyuan He ◽  
Duyan Bi ◽  
Shan Gao
Keyword(s):  
Standard Deviation ◽  
Mean Value ◽  
Experimental Results ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Color Distortion ◽  
Local Media ◽  
Dark Channel

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

scholarly journals Single Image Dehazing Algorithm Based on Modified Dark Channel Prior

2021 ◽  
Vol E104.D (10) ◽  
pp. 1758-1761
Author(s):  
Hao ZHOU ◽  
Zhuangzhuang ZHANG ◽  
Yun LIU ◽  
Meiyan XUAN ◽  
Weiwei JIANG ◽  
...  
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

Single Image Dehazing with Dark Channel Prior

2021 ◽  
Author(s):  
Jaspreet Kaur ◽  
Srishti Sabharwal ◽  
Ayush Dogra ◽  
Bhawna Goyal ◽  
Rohit Anand
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

Single Image Dehazing Algorithm Based on Adaptive Dark Channel Prior

2018 ◽  
Vol 47 (2) ◽  
pp. 210001
Author(s):  
刘国 LIU Guo ◽  
吕群波 L Qun bo ◽  
刘扬阳 LIU Yang yang
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

Improved method for single image dehazing using dark channel prior

2016 ◽  
Vol 31 (8) ◽  
pp. 840-845 ◽  
Author(s):  
王凯 WANG Kai ◽  
王延杰 WANG Yan-jie ◽  
樊博 FAN Bo
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Improved Method ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

scholarly journals Unsupervised Single Image Dehazing Using Dark Channel Prior Loss

2020 ◽  
Vol 29 ◽  
pp. 2692-2701 ◽  
Author(s):  
Alona Golts ◽  
Daniel Freedman ◽  
Michael Elad
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

scholarly journals Transmission Estimation by Complex Assumption with Occlusion Handling

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang ◽  
Mingzhu Zhu ◽  
Bingwei He
Keyword(s):  
Optimal Solution ◽  
Image Sensor ◽  
Natural Images ◽  
Single Image ◽  
Occlusion Handling ◽  
Image Dehazing ◽  
Haze Removal ◽  
Dark Channel ◽  
Transmission Estimation ◽  
Single Image Dehazing

Transmission estimation is a critical step in single-image dehazing. The estimate of each pixel describes the portion of the scene radiance that is degraded by hazing and finally reaches the image sensor. Transmission estimation is an underconstrained problem, and, thus, various assumptions, priors, and models are employed to make it solvable. However, most of the previous methods did not consider the different assumptions simultaneously, which, therefore, did not correctly reflect the previous assumptions in the final result. This paper focuses on this problem and proposes a method using an energy function that clearly defines the optimal transmission map and combines the assumptions from three aspects: fidelity, smoothness, and occlusion handling, simultaneously. Fidelity is measured by a novel principle derived from the dark channel prior, smoothness is described by the assumption of piecewise smoothening, and occlusion handling is achieved based on a new proposed feature. The transmissions are estimated by searching for the optimal solution of the function that can retain all the employed assumptions simultaneously. The proposed method is evaluated on the synthetic images of two datasets and various natural images. The results show that there is remarkable fidelity and smoothness in the transmission and that a good performance is exhibited for haze removal.

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