| 引用本文: | 黄鹤,李战一,胡凯益,王会峰,茹锋,王珺.融合大气光值-图估计的无人机航拍图像去雾[J].哈尔滨工业大学学报,2023,55(5):88.DOI:10.11918/202111001 |
| HUANG He,LI Zhanyi,HU Kaiyi,WANG Huifeng,RU Feng,WANG Jun.UAV aerial image dehazing by fusion of atmospheric light value and graph estimation[J].Journal of Harbin Institute of Technology,2023,55(5):88.DOI:10.11918/202111001 |
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| 融合大气光值-图估计的无人机航拍图像去雾 |
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黄鹤1,2,李战一1,胡凯益1,王会峰1,茹锋1,2,王珺2
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(1.长安大学 电子与控制工程学院,西安 710064;2.西安市智慧高速公路信息融合与控制重点实验室(长安大学),西安 710064)
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| 摘要: |
| 针对现有去雾算法大都存在复原图像亮度低、天空明显色彩失真等问题,提出了一种融合大气光值-图估计的无人机航拍图像去雾方法。首先,根据颜色衰减先验理论获取景深图像,将景深图像内偏差最小区域均值作为大气光值；其次,设计了一种自适应随机游走聚类方法用来估计大气光图,通过自适应随机游走算法将图像聚类为N个子区域,对子区域前0.1%像素求均值作为区域大气光值,将区域大气光值组合并通过引导滤波对其进行细化,获得大气光图；然后,通过融合大气光值-图估计方法将两种大气光估计融合为新的大气光图,作为更加准确的大气光估计；利用雾霾线先验方法获得透射率,同时提出一种暗补偿方法对其进行优化,提高透射率精度；最后,根据大气散射模型,利用求得的融合大气光图和优化透射率,得到清晰的复原图像。实验结果表明,相对于比较算法,提出的算法的复原图像在信息熵、平均梯度、模糊系数及对比度上分别提升1.1%、6.3%、8.5%、6.4%,主观视觉效果更好,信息更加丰富。 |
| 关键词: 图像去雾 随机游走 颜色衰减先验 雾霾线先验 无人机 |
| DOI：10.11918/202111001 |
| 分类号:TP391.4 |
| 文献标识码:A |
| 基金项目:国家自然科学基金面上项目(52172379);国家重点研发计划(2021YFB2501200);陕西省重点研发计划(2021SF-483);陕西省自然科学基础研究计划(2021JM-184);长安大学中央高校基本科研业务费专项(1,1);西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金(300102321502) |
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| UAV aerial image dehazing by fusion of atmospheric light value and graph estimation |
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HUANG He1,2,LI Zhanyi1,HU Kaiyi1,WANG Huifeng1,RU Feng1,2,WANG Jun2
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(1.School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China; 2.Xi’an Key Laboratory of Intelligent Expressway Information Fusion and Control (Chang’an University), Xi’an 710064, China)
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| Abstract: |
| In view of the problems of low brightness and obvious color distortion of the sky in restored images in most existing algorithms for image dehazing, a haze removal method for UAV aerial images based on atmospheric light value and graph estimation was proposed. First, the depth-of-field image was obtained according to the color attenuation prior theory, and the mean value of the region with the minimum deviation in the depth-of-field image was taken as the atmospheric light value. Then, a random walk clustering method was designed to estimate the atmospheric light map. The random walk algorithm was used to cluster the image into N sub-regions, and the mean value of the first 0.1% pixels of the sub-regions was taken as the regional atmospheric light value, which was then combined and refined by guided filtering to obtain the atmospheric light map. Next, the two atmospheric light estimators were fused into a new atmospheric light map with atmospheric light valuegraph estimation, which is a more accurate atmospheric light estimator. The transmittance was obtained by haze-lines prior method, and a dark compensation method was proposed to improve the transmission accuracy. Finally, according to the atmospheric scattering model, a clear restored image was obtained based on the fused atmospheric light map and optimized transmittance. Experimental results show that compared with other algorithms, the proposed algorithm improved the information entropy, mean gradient, blur coefficient, and contrast by 1.1%, 6.3%, 8.5%, and 6.4%, respectively, with better subjective visual effect and more abundant information. |
| Key words: image dehazing random walk color attenuation prior haze-lines prior UAV |
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