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Patch-Based dark channel prior Dehazing for RS Multi-spectral Image

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Chinese Journal of Electronics 2015年第3期24卷 573-578页

作者： WANG Lixia XIE Weixin PEI Jihong ATR Key Laboratory of National Defense Technology College of Information Engineering Shenzhen University

Remote sensing(RS) multi-spectral images are usually suffered from cloud and fog cover, which can lead to analysis troubles and application limitations. A novel patch-based dark channel prior dehaze method is proposed for solving this problem. An Atmospheric light(AL) curved surface hypothesis, instead of globally invariable plane, is applied to describe AL distribution, and a patch-based approach is given to estimate curved *** using AL curved surface estimation, a new recovering model for RS multi-spectral images is given to obtain dehaze-free images. Comparative experiments are conducted, those results illustrate that the proposed method can produces visually impressive restored images, and the proposed method is superior to other relative methods in terms of image quality evaluations.

关键词： RS multi-spectral images Dehaze Cloud and fog removal dark channel prior AL curved surface

Single Image Dehazing Based on Combining dark channel prior and Scene Radiance Constraint

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Chinese Journal of Electronics 2016年第6期25卷 1114-1120页

作者： ZENG Liang DAI Yongzhen College of Computer National University of Defense Technology

Images captured in foggy or hazy weather conditions often suffer from poor visibility. The dark channel prior method has well solved the single image dehazing problem in nature, but it is invalid when the scene objects are inherently similar to the atmospheric light and no shadow is cast on them. We propose an efficient regularization method by adding a scene radiance constraint and combing the dark channel prior to remove hazes from a single input image. The experiments show that this improved algorithm can deal with various levels of foggy weather conditions, as well as greatly enhance the image's visibility and details. In addition, the recovered haze-free image has little or no halo artifacts.

关键词： Image dehazing Scene radiance constraint dark channel prior Visibility enhancement Detail augment

Image Dehazing by Incorporating Markov Random Field with dark channel prior

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Journal of Ocean University of China 2020年第3期19卷 551-560页

作者： XU Hao TAN Yibo WANG Wenzong WANG Guoyu College of Information Science and Engineering Ocean University of ChinaQingdao 266100China

As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.

关键词： image dehazing dark channel prior Markov random field image segmentation

Fog removal and enhancement method for UAV aerial images based on dark channel prior

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Journal of Control and Decision 2023年第2期10卷 188-197页

作者： Fei Xia Hu Song Haoxiang Dou Information Operation Inspection Center State Grid Jiangsu Information&Telecommunication CompanyNanjingPeople’s Republic of China

The existing UAV aerial image de-fog methods have low image contrast after de-fog,the difference between light and dark image is not obvious,leading to poor de-fog ***,an aerial image de-fog enhancement method based on dark channel a priori is *** image variance and absolute gradient mean are combined to get the weight coefficients,and the edge pixels are smoothed by using the multiple decomposition *** image intensity is calculated and the noise is reduced.A convolution neural network is introduced to calculate the atmospheric transmittance in *** on this,dark channel prior algorithm is used to enhance the light and shade difference of aerial photography image and realise the de-fog enhancement of aerial photography *** verify the performance of the proposed method,simulation experiments are designed which were compared with the existing methods results in better fog-removing effect,higher contrast and shorter time.

关键词： dark channel prior unmanned aerial vehicle(UAV) aerial image fog enhancement halo artefact image denoising

Image Defogging Algorithm Based on Sky Region Segmentation and dark channel prior

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Journal of Systems Science and Information 2020年第5期11卷 476-486页

作者： Zuyun JIANG Xiangdong SUN Xiaochun WANG College of Sciences Beijing Forestry UniversityBeijing 100083China College of Sciences North China University of TechnologyBeijing 100144China

Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color ***,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 *** this reason,our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component *** on the segmentation result,the initial transmission map is modified in turn,and finally refined by the guided *** 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 *** results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.

关键词： dark channel prior image haze removal image segmentation transmission map V-transform

Accelerated haze removal for a single image by dark channel prior

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Frontiers of Information Technology & Electronic Engineering 2019年第8期20卷 1109-1119页

作者： Bo-xuan YUE Kang-ling LIU Zi-yang WANG Jun LIANG State Key Laboratory of Industrial Control Technology Zhejiang University

Haze scatters light transmitted in the air and reduces the visibility of *** with haze is still a challenge for image processing applications *** the purpose of haze removal,we propose an accelerated dehazing method based on single *** other methods based on regions,our method estimates the transmission map and atmospheric light for each pixel independently,so that all parameters can be evaluated in one traverse,which is a key to ***,the transmission map is bilaterally filtered to restore the relationship between *** restoration via the linear hazy model,the restored images are tuned to improve the contrast,value,and saturation,in particular to offset the intensity errors in different channels caused by the corresponding *** experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing *** with other dehazing methods and quantitative criteria(peak signal-to-noise ratio,detectable marginal rate,and information entropy difference)are introduced to verify its performance.

关键词： Haze removal dark channel prior Hazy image model Bilateral filtering

Underwater Image Dehazing Using Modified dark channel prior

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Underwater Image Dehazing Using Modified Dark Channel Prior

一种基于dark channel prior图像去雾改进算法

第30届中国控制与决策会议

作者： Bowen Yao Ji Xiang College of Electrical Engineering Zhejiang University

In an underwater environment, light always scatters and absorbs as it travels from the object to the camera,seriously affecting image quality. As a result, underwater images have poorer contrast, like a haze covering them. In this paper, a modified method for images restoration based on the dark channel prior（DCP） is presented. Firstly, the ambient light is estimated according to the difference between the blue and red channel. Then, attenuations of three RGB channels are obtained separately. Finally, a color collection is used to compensate the remaining color distortion. The experiments carried out at last demonstrate the good performance of this method for improving the visibility of underwater images.

关键词： Underwater Image Haze Removal dark channel prior

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计算机科学与应用 2020年第12期10卷 2189-2196页

作者：阳小燕许伦辉宗建华夏国清广州大学华软软件学院广东广州华南理工大学土木与交通学院广东广州

探测机器人视觉导航中,现场烟雾或者有色气体会直接导致回传的视频图像雾化且充满噪声,图像品质差,影响后续目标检测与跟踪。针对上述问题,本文提出了一种基于dark channel prior图像去雾改进算法,该算法首先对采集到图像进行灰度化预处... 详细信息

探测机器人视觉导航中,现场烟雾或者有色气体会直接导致回传的视频图像雾化且充满噪声,图像品质差,影响后续目标检测与跟踪。针对上述问题,本文提出了一种基于dark channel prior图像去雾改进算法,该算法首先对采集到图像进行灰度化预处理,再用滤波器进行去噪处理,有效去除采图像中的噪声,使待处理图像更加平滑,最后使用dark channel prior去雾算法进行去雾处理,得到高品质图像。根据仿真实验表明,本算法能够有效解决图像去雾中的噪声问题,提高传送画面的清晰度,并缩短图像处理时间,时实性更强。

关键词：图像去雾 dark channel prior 滤波去噪

Velocity Calculation by Automatic Camera Calibration Based on Homogenous Fog Weather Condition

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International Journal of Automation and computing 2013年第2期10卷 143-156页

作者： Hong-Jun Song Yang-Zhou Chen Yuan-Yuan Gao Center for Autonomous Technology and Intelligent Control Beijing University of Technology College of Information and Engineering Zhejiang Agriculture and Forestry University

A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper. Camera fixed in the middle of the road should be calibrated in homogenous fog weather condition, and can be used in any weather condition. Unlike other researches in velocity calculation area, our traffic model only includes road plane and vehicles in motion. Painted lines in scene image are neglected because sometimes there are no traffic lanes, especially in un-structured traffic scene. Once calibrated, scene distance will be got and can be used to calculate vehicles average velocity. Three major steps are included in our algorithm. Firstly, current video frame is recognized to discriminate current weather condition based on area search method （ASM）. If it is homogenous fog, average pixel value from top to bottom in the selected area will change in the form of edge spread function （ESF）. Secondly, traffic road surface plane will be found by generating activity map created by calculating the expected value of the absolute intensity difference between two adjacent frames. Finally, scene transmission image is got by dark channel prior theory, camera s intrinsic and extrinsic parameters are calculated based on the parameter calibration formula deduced from monocular model and scene transmission image. In this step, several key points with particular transmission value for generating necessary calculation equations on road surface are selected to calibrate the camera. Vehicles pixel coordinates are transformed to camera coordinates. Distance between vehicles and the camera will be calculated, and then average velocity for each vehicle is got. At the end of this paper, calibration results and vehicles velocity data for nine vehicles in different weather conditions are given. Comparison with other algorithms verifies the effectiveness of our algorithm.

关键词： Vehicle velocity calculation homogenous fog weather condition dark channel prior monocular camera calibration