| 雾天室外场景光照参数估计 |
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| 引用本文： | 付文晓,张锐,林丽丽,钟凡,彭群生,秦学英.雾天室外场景光照参数估计[J].软件学报,2014,25(S2):268-277. |
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| 作者姓名： | 付文晓 张锐 林丽丽 钟凡 彭群生 秦学英 |
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| 作者单位： | 山东大学 计算机科学与技术学院, 山东 济南 250101,山东财经大学 计算机科学与技术学院, 山东 济南 250014,山东大学 计算机科学与技术学院, 山东 济南 250101,山东大学 计算机科学与技术学院, 山东 济南 250101,浙江大学 CAD&CG国家重点实验室, 浙江 杭州 310058,山东大学 计算机科学与技术学院, 山东 济南 250101;山东省软件工程重点实验室, 山东 济南 250101 |
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| 基金项目： | 国家自然科学基金(61173070,61202149,61272431,61303089) |
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| 摘 要： | 在雾天情况下,雾对光线的散射使得室外场景的光照发生很大变化,太阳光和天空光的参数估计变得更为复杂.结合雾天情况下的大气散射模型,提出了室外场景的雾天基图像模型,并基于该模型提出了雾天室外场景图像光照参数估计算法.在已知场景基图像的条件下,利用迭代散射系数方法,优化求解雾浓度与场景深度图像,然后通过对去雾图像进行分解,获得最佳的去雾图像以及正确的光照分解系数.算法能够得到较为精确的雾浓度与场景深度图像.实验结果表明了算法的有效性.
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| 关 键 词： | 光照估计 雾天室外场景 基图像 深度图像 图像分解 |
| 收稿时间： | 5/9/2014 12:00:00 AM |
| 修稿时间： | 2014/8/19 0:00:00 |
Illumination Estimation of Hazing Outdoor Scene |
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| FU Wen-Xiao,ZHANG Rui,LIN Li-Li,ZHONG Fan,PENG Qun-Sheng and QIN Xue-Ying.Illumination Estimation of Hazing Outdoor Scene[J].Journal of Software,2014,25(S2):268-277. |
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| Authors: | FU Wen-Xiao ZHANG Rui LIN Li-Li ZHONG Fan PENG Qun-Sheng and QIN Xue-Ying |
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| Affiliation: | School of Computer Science and Technology, Shandong University, Ji'nan 250101, China,School of Computer Science and Technology, Shandong University of Finance and Economics, Ji'nan 250014, China,School of Computer Science and Technology, Shandong University, Ji'nan 250101, China,School of Computer Science and Technology, Shandong University, Ji'nan 250101, China,State Key Laboratory of CAD&CG, Zhejiang University, Hang zhou 310058, China and School of Computer Science and Technology, Shandong University, Ji'nan 250101, China;Shandong Provincial Key Laboratory of Software Engineering, Ji'nan 250101, China |
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| Abstract: | In hazy weather condition, images of outdoor scene are greatly affected by light scattering. Due to the fog, image parameter estimation of sunlight and skylight becomes more complex. In accordance with the atmospheric scattering model, this paper formulates the basis image model of outdoor scene, and presents the algorithm of parameter estimation of haze image. By iterating the scattering coefficient, the algorithm produces the proper haze density and depth of the haze image. It then decomposes the haze-free image to obtain the right illumination coefficient, best haze-free image and combined image. Experimental results demonstrate the effectiveness of the proposed approach in finding the proper hazy density and the corrected depth image of the scene. |
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| Keywords: | illumination estimation hazing outdoor scene basis image depth image image decomposition |
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