Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

作     者：Xiuyuan Wang Chenghai Yang Jian Zhang Huaibo Song 

作者机构：College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of ThingsMinistry of AgricultureYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent ServiceYanglingShaanxi 712100China USDA-ARS Southern Plains Agricultural Research Center3103 F and B RoadCollege StationTexas 77845USA College of Resource and EnvironmentHuazhong Agricultural UniversityWuhan 430070China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2018年第11卷第2期

页      面：170-176页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA10230402) Agricultural Science and Technology Project of Shaanxi Province(No.2016NY-157) Fundamental Research Funds Central Universities(2452016077) 

主　　题：agricultural monitoring image dehazing monitoring image dark channel prior(DCP) brightness promoting 

摘      要：Obtaining clear and true images is a basic requirement for agricultural ***,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target *** image dehazing methods based on image enhancement technology can cause the loss of image information and image *** order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was *** enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were *** different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP *** image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing *** showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP *** performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.