Fast image dehazing using improved dark channel prior

Xu, Haoran; Guo, Jianming; Liu, Qing; Ye, Ling-li

doi:10.1109/icist.2012.6221729

Cited by 121 publications

(61 citation statements)

References 11 publications

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“…[18] 4) Improved Dark Channel: The dark channel prior developed by Haoran Xu for dehazing the single image by combining the bilateral filtering with dark channel prior in this algorithm start with the atmospheric scattering model then estimate the transmission map by using DCP combine with grayscale to extract refine transmission map by using the fast bilateral filter . This algorithm has a fast execution speed then original DCP algorithm and greatly improves visual appearance as shown in fig : 2 (a) , (b) [17]. Artificial bee colony optimization algorithm the position of food source represent the possible solution of optimization problem and the nectar amount of food source correspond to the quality of the association solution.…”

Section: A Prior Based Methodsmentioning

confidence: 99%

A Comprehensive Study on Various Haze Removal and Visibility Restoration Techniques for Image Processing

2017

IJRASET

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Section: A Prior Based Methodsmentioning

confidence: 99%

A Comprehensive Study on Various Haze Removal and Visibility Restoration Techniques for Image Processing

2017

IJRASET

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“…This implies that the pixel values of the dark channel can serve as an important clue to estimate the haze density. Successful dehazing results of various DCP-based dehazing algorithms [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] support the effectiveness of the DCP in image dehazing.…”

Section: Dark Channel Prior (Dcp)mentioning

confidence: 99%

A review on dark channel prior based image dehazing algorithms

Lee

Yun

Nam³

et al. 2016

J Image Video Proc.

178

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The presence of haze in the atmosphere degrades the quality of images captured by visible camera sensors. The removal of haze, called dehazing, is typically performed under the physical degradation model, which necessitates a solution of an ill-posed inverse problem. To relieve the difficulty of the inverse problem, a novel prior called dark channel prior (DCP) was recently proposed and has received a great deal of attention. The DCP is derived from the characteristic of natural outdoor images that the intensity value of at least one color channel within a local window is close to zero. Based on the DCP, the dehazing is accomplished through four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This four-step dehazing process makes it possible to provide a step-by-step approach to the complex solution of the ill-posed inverse problem. This also enables us to shed light on the systematic contributions of recent researches related to the DCP for each step of the dehazing process. Our detailed survey and experimental analysis on DCP-based methods will help readers understand the effectiveness of the individual step of the dehazing process and will facilitate development of advanced dehazing algorithms.

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“…A median of 0.1% of pixels with the highest dark channel values is used to arrive at an estimate. The taking of the median of 0.1% of the pixels avoids the distortion of the colors maintaining the quality of the images [7]. The image is then put through testing wherea white-balance correction is applied with the atmospheric light (A) set to the initial image hence the patches get the faint white light same as the machine training procedure.…”

Section: B Haze Removal Using Random Forest Regressormentioning

confidence: 99%

Application of Machine Learning Based Random Forest Regressor in Image Dehazing

Khan

2018

ijarcs

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: Low visibility causes haze in images due to fog or dust particles in the atmosphere. The haze causes color distortion and even blurring in the images captured. Machine learning approach has been considered to provide optimized haze removal results to generate higher quality images from where information can be extracted. In this context, machine learning-based random forest regressor algorithm along with postprocessing techniques was proposed as a superior solution for de-hazing images and thereby generating higher quality images in comparison to other direct de-hazing methods.

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Fast image dehazing using improved dark channel prior

Cited by 121 publications

References 11 publications

A Comprehensive Study on Various Haze Removal and Visibility Restoration Techniques for Image Processing

A Comprehensive Study on Various Haze Removal and Visibility Restoration Techniques for Image Processing

A review on dark channel prior based image dehazing algorithms

Application of Machine Learning Based Random Forest Regressor in Image Dehazing

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