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收稿：2018-07-18，
修回：2018-8-20，
纸质出版：2019-02-16
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目的
2
图像去雾是降低雾、霾、沙等低能见度成像环境对图像的退化影响，提高图像信息获取质量的过程。为了消除先验盲区，同时进一步提高去雾图像边缘细节的清晰度，提出一种混合先验与加权引导滤波的图像去雾算法。
方法
2
首先改进大气光值估计方法，提高大气光值估计的准确性。然后利用混合先验理论求取双约束区域的大气透射率，一定程度上消除了先验盲区，提高了去雾算法的鲁棒性。最后利用加权引导滤波算法优化透射率图，提高了图像边缘细节的清晰度。
结果
2
本文以通用去雾测试图像和小型无人机拍摄的雾天图像作为实验对象，通过对比分析4种组合步骤算法的复原效果，验证本文各步骤改进方法的合理性与整体算法的优越性。实验结果表明：混合先验理论改善了暗原色先验在明亮区域的失真现象和颜色衰减先验对浓雾处理上的不足，取得了较好的视觉效果；加权引导滤波改善了图像边缘模糊的现象，使复原后的图像边缘细节更加清晰；相较传统算法，本文算法视觉效果更好，去雾图像边缘细节更加明显，综合评价指标均值提升幅度较大。
结论
2
针对有雾图像复原，通过理论分析和实验验证，说明了本文各步骤的改进具有一定的优越性，所提的算法具有较强的鲁棒性。
Objective
2
Image dehazing is a process of reducing the degradation effect of low-visibility imaging environment
such as fog
sputum
and sand
and improving the quality of image information acquisition. Image dehazing mainly solves the problems of image feature information blur
low contrast
gray-level concentration
and color distortion. At present
image dehazing methods are mainly divided into two categories
namely
image restoration and image enhancement. A dehazing algorithm based on image restoration is used to establish a physical model of image degradation in restoring the clear image in a targeted manner by analyzing the degradation mechanism of the image and by using prior knowledge or assumptions. The dehazing algorithm is more targeted compared with image enhancement algorithms. Deblurring is better and image information is complete
which should be investigated. Therefore
an image dehazing algorithm based on mixed prior and weighted guided filter (MPWGF) is proposed to eliminate the prior blind zone and improve the sharpness of the edge detail of haze-free images.
Method
2
First
a new method of atmospheric light value estimation is proposed to reduce the limitation of atmospheric light value estimation and utilize the advantage of mixed prior conditions. Pixel positions of 0.1% before brightness in dark channel and depth maps are extracted
and coordinate points extracted from the two images are compared. The coordinate points are retained when two images are observed simultaneously; otherwise
the values with the highest brightness that correspond to the remaining coordinate points in the original image are eliminated. This method can eliminate outliers to some extent and improves the accuracy of atmospheric light estimation. Then
mixed prior theory is used to calculate the atmospheric transmission of the double-constraint region
which eliminates the prior blind zone to a certain extent. This theory improves the robustness of the dehazing algorithm. The dark channel prior (DCP) and color attenuation prior (CAP) have good recovery effects and can compensate for the existence of the prior blind zone. Therefore
an effective region segmentation method is proposed to segment the bright and foggy regions of blurred images. On the basis of regional characteristics
DCP and CAP are used to obtain atmospheric transmittance maps to solve the prior blind region problem in which a single prior estimation method affects the robustness of the restoration algorithm. Finally
an adaptive guided filter algorithm is used to optimize the transmission map
which improves the sharpness of the image edge details. The obtained coarse transmittance map should be refined to eliminate the halo and block artifacts that locally exist in the restored image. A traditional transmittance map optimization algorithm has poor edge retention capability and serious loss of details. Thus
this study proposes an adaptive weighted guidance filtering algorithm based on the traditional algorithm. The edge detail improvement of the fine transmittance map is achieved by adding an adaptive weighted factor.
Result
2
In this study
the general dehazing test image and foggy image captured by a small UAV are taken as experimental objects. The rationality of the improved methods and the superiority of the overall algorithm are verified by comparing and analyzing the restoration effects of the four combined step algorithms. Experimental results show that the mixed prior theory improves the distortion of the dark priori in the bright region and the deficiency of CAP in dense fog processing and achieves better visual effect. Weighted guided filtering improves the image edge blurring and makes the image edge details clear after restoration. In comparison with other comparison algorithms
the proposed method has better visual effects
and the edge details of haze-free images are more evident. The average increase of comprehensive evaluation index is large.
Conclusion
2
For the restoration of hazy images
the superiority of the proposed improved model is demonstrated through theoretical analysis and experimental verification. The restoration of the proposed algorithm is better than that with the traditional algorithm. The main conclusions are summarized as follows. Mixed prior theory can improve the prior blind area problem in DCP and CAP theories to a certain extent
and the effect of image defogging is better. The adaptive guided filtering algorithm can optimize the transmittance image better and improve the edge sharpness of the defogging image. The image defogging effect can be improved by combining hybrid prior theory with the adaptive guided filtering algorithm. Under the same conditions
the proposed algorithm has better image restoration effect compared with the traditional fog removal algorithm. In this study
several limitations are observed in the parameter setting of regional segmentation
such as the adjustment of parameters through experience and insufficient certain theoretical basis. The parameter setting will be investigated in our future study. MPWGF has broad application prospects in image restoration
artificial intelligence
photogrammetry
and other fields.
Xiao J S, Gao W, Zou B Y, et al. Image dehazing based on sky-constrained dark channel prior[J]. Acta Electronica Sinica, 2017, 45(2):346-352.
肖进胜, 高威, 邹白昱, 等.基于天空约束暗通道先验的图像去雾[J].电子学报, 2017, 45(2):346-352. [DOI:10.3969/j.issn.0372-2112.2017.02.012]
Ancuti C, Ancuti C O, Timofte R, et al. NTIRE 2018 challenge on image dehazing: methods and results[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake: IEEE, 2018: 891-901.
Wu D, Zhu Q S. The latest research progress of image dehazing[J]. Acta Automatica Sinica, 2015, 41(2):221-239.
吴迪, 朱青松.图像去雾的最新研究进展[J].自动化学报, 2015, 41(2):221-239. [DOI:10.16383/j.aas.2015.c131137]
Tan R T. Visibility in bad weather from a single image[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA: IEEE, 2008: 1-8.[ DOI:10.1109/CVPR.2008.4587643 http://dx.doi.org/10.1109/CVPR.2008.4587643 ]
Fattal R. Single image dehazing[J]. ACM Transactions on Graphics (TOG), 2008, 24(3):#72.[DOI:10.1145/1360612.1360671]
He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 1956-1963.[ DOI:10.1109/CVPR.2009.5206515 http://dx.doi.org/10.1109/CVPR.2009.5206515 ]
Xu H R, Guo J M, Liu Q, et al. Fast image dehazing using improved dark channel prior[C]//Proceedings of 2012 IEEE International Conference on Information Science and Technology. Hubei, China: IEEE, 2012: 663-667.[ DOI:10.1109/ICIST.2012.6221729 http://dx.doi.org/10.1109/ICIST.2012.6221729 ]
Gibson K B, Võ D T, Nguyen T Q. An investigation of dehazing effects on image and video coding[J]. IEEE Transactions on Image Processing, 2012, 21(2):662-673.[DOI:10.1109/TIP.2011.2166968]
Wang Y F, Yin C L, Huang Y M, et al. Image haze removal using a bilateral filter[J]. Journal of Image and Graphics, 2014, 19(3):386-392.
王一帆, 尹传历, 黄义明, 等.基于双边滤波的图像去雾[J].中国图象图形学报, 2014, 19(3):386-392. [DOI:10.11834/jig.20140307]
He K M, Sun J, Tang X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1397-1409.[DOI:10.1109/TPAMI.2012.213]
Zhu Q S, Mai J M, Shao L. A Fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 2015, 24(11):3522-3533.[DOI:10.1109/TIP.2015.2446191]
Shen Y Y, Liu C X, Zhang J D, et al. Atmospheric light correction and transmission optimization based robust image dehazing[J]. Journal of Computer-Aided Design&Computer Graphics, 2017, 29(9):1604-1612.
沈逸云, 刘春晓, 张金栋, 等.鲁棒图像去雾的大气光校正与透射率优化算法[J].计算机辅助设计与图形学学报, 2017, 29(9):1604-1612. [DOI:10.3969/j.issn.1003-9775.2017.09.003]
Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66.[DOI:10.1109/TSMC.1979.4310076]
Xie W, Zhou Y Q, You M. Improved guided image filtering integrated with gradient information[J]. Journal of Image and Graphics, 2016, 21(9):1119-1126.
谢伟, 周玉钦, 游敏.融合梯度信息的改进引导滤波[J].中国图象图形学报, 2016, 21(9):1119-1126. [DOI:10.11834/jig.20160901]
Li J T, Zhang Y J. Improvements of image haze removal algorithm and its subjective and objective performance evaluation[J]. Optics and Precision Engineering, 2017, 25(3):735-741.
李佳童, 章毓晋.图像去雾算法的改进和主客观性能评价[J].光学精密工程, 2017, 25(3):735-741. [DOI:10.3788/OPE.20172503.0735]
Huang Y Q, Ding W R, Li H G. Haze removal for UAV reconnaissance images using layered scattering model[J]. Chinese Journal of Aeronautics, 2016, 29(2):502-511.[DOI:10.1016/j.cja.2016.01.012]
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