基于改进Criminisi算法的地基云图修复方法
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摘要
全天空成像仪(Total sky imager,TSI)对天空进行观测时,设备的结构特点会使采集到的云图信息不完整,对图像的分析造成不利影响。针对Criminisi算法修复地基云图所造成修复顺序发生错误、图像不连续以及匹配块遍历搜索时间复杂度大的问题,本文提出了一种基于改进Criminisi算法的地基云图修复方法。该算法改进了优先权计算公式,引入地基云图独特的红蓝比特征作为置信项,使得含有更多信息的像素块具有更高的优先级,在搜索匹配块的过程中,基于启发信息选择匹配区域的大小,避免了搜索到离待修复块较远的相关性较低的匹配块,也有效缩短了匹配块搜索时间,降低了算法的时间复杂度。实验结果表明,改进后的Criminisi算法具有较好的图像修复效果,且降低了时间复杂度,提高了修复效率。
When the total sky imager(TSI) is used to observe the sky,the structural characteristics of the device will make the collected cloud image information incomplete,which affects the analysis of images. In order to deal with the problems,including the wrong order due to the sharp decrease to zero of the confidence level,the discontinuity of image and the large complexity of time for traversal searching the matching block in the process of repairing ground-based cloud image by the Criminisi algorithm,we propose a ground-based cloud image inpainting method based on the improved Criminisi algorithm in this paper. The calculation formula of priority is improved,and the unique red-blue ratio feature of the groundbased cloud map is introduced as a confidence term,so that the pixel block with more information has higher priority. In the process of searching for the matching block,the searching area is selected based on heuristic information in order to avoid the blocks far away from the block to be repaired and those with low correlation,which effectively shortens the searching time and reduces the time complexity of the algorithm.Experimental results show that the improved Criminisi algorithm has better image restoration effect,can reduce the time complexity and improve the image inpainting efficiency.
When the total sky imager(TSI) is used to observe the sky,the structural characteristics of the device will make the collected cloud image information incomplete,which affects the analysis of images. In order to deal with the problems,including the wrong order due to the sharp decrease to zero of the confidence level,the discontinuity of image and the large complexity of time for traversal searching the matching block in the process of repairing ground-based cloud image by the Criminisi algorithm,we propose a ground-based cloud image inpainting method based on the improved Criminisi algorithm in this paper. The calculation formula of priority is improved,and the unique red-blue ratio feature of the groundbased cloud map is introduced as a confidence term,so that the pixel block with more information has higher priority. In the process of searching for the matching block,the searching area is selected based on heuristic information in order to avoid the blocks far away from the block to be repaired and those with low correlation,which effectively shortens the searching time and reduces the time complexity of the algorithm.Experimental results show that the improved Criminisi algorithm has better image restoration effect,can reduce the time complexity and improve the image inpainting efficiency.
引文
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[15]朱想,周海,丁杰,等.全天空云图图像复原算法[J].计算机辅助设计与图形学学报,2014,26(6):896-902.Zhu Xiang,Zhou Hai,Ding Jie,et al.All-sky cloud map image restoration algorithm research[J].Journal of Computer-Aided Design&Computer Graphics,2014,26(6):896-902.
[2]谭津,邓长虹,杨威,等.微电网光伏发电的Adaboost天气聚类超短期预测方法[J].电力系统自动化,2017,41(21):33-39.Tan Jin,Deng Changhong,Yang Wei,et al.Ultra-short-term photovoltaic power forecasting in microgrid based on Adaboost clustering[J].Automation of Electric Power Systems,2017,41(21):33-39.
[3]朱想,居蓉蓉,程序,等.组合数值天气预报与地基云图的光伏超短期功率预测模型[J].电力系统自动化,2015,39(6):4-10.Zhu Xiang,Ju Rongrong,Cheng Xu,et al.A very short-term prediction model for photovoltaic power based on numerical weather prediction and ground-based cloud images[J].Automation of Electric Power Systems,2015,39(6):4-10.
[4]陈志宝,李秋水,程序,等.基于地基云图的光伏功率超短期预测模型[J].电力系统自动化,2013,37(19):20-25.Chen Zhibao,Li Qiushui,Cheng Xu,et al.A very short-term prediction model for photovoltaic power based on ground-based cloud images[J].Automation of Electric Power Systems,2013,37(19):20-25.
[5]Marquez R,Coimbra C F M.Intra-hour DNI forecasting based on cloud tracking image analysis[J].Solar Energy,2013,91(3):327-336.
[6]Guillemot C,Meur O L.Image inpainting:Overview and recent advances[J].IEEE Signal Processing Magazine,2013,31(1):127-144.
[7]Bertalmio M,Sapiro G,Caselles V,et al.Image inpainting[C]//SIGGRAPH’00:Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques.New York,USA:ACM Press/Addison-Wesley Publishing Co,2000:417-424.
[8]Chan T F,Shen J H.Mathematical models for local nontexture inpaintings[J].SIAM Journal on Applied Mathematics,2001,62(3):1019-1043.
[9]Chan T F,Shen J H.Nontexture inpainting by curvature-driven diffusions[J].Journal of Visual Communication&Image Representation,2001,12(4):436-449.
[10]Criminisi A,Perez P,Toyama K.Region filling and object removal by exemplar-based Image inpainting[J].IEEE Transactions on Image Processing,2004,13(9):1200-1212.
[11]彭坤杨,董兰芳.一种基于图像平均灰度值的快速图像修复算法[J].中国图象图形学报,2010,15(1):50-55.Peng Kunyang,Dong Lanfang.A fast image inpainting algorithm based on average gray value[J].Journal of Image and Graphics,2010,15(1):50-55.
[12]朱霞,李宏,张卫.一种基于颜色区域分割的图像修复算法[J].计算机工程,2008,34(14):191-193.Zhu Xia,Li Hong,Zhang Wei.Image inpainting algorithm based on color region segmentation[J].Computer Engineering,2008,34(14):191-193.
[13]韩明珠,郭树旭,藏玲玲,等.基于样本块的图像修复改进算法[J].计算机工程与应用,2015,51(16):192-194.Han Mingzhu,Guo Shuxu,Zang Lingling,et al.Improved exemplar block-based image inpainting algorithm[J].Computer Engineering and Applications,2015,51(16):192-194.
[14]Liu Y,Caselles V.Exemplar-based image inpainting using multiscale graph cuts[J].IEEE Transactions on Image Processing,2013,22(5):1699-1711.
[15]朱想,周海,丁杰,等.全天空云图图像复原算法[J].计算机辅助设计与图形学学报,2014,26(6):896-902.Zhu Xiang,Zhou Hai,Ding Jie,et al.All-sky cloud map image restoration algorithm research[J].Journal of Computer-Aided Design&Computer Graphics,2014,26(6):896-902.