光照不均匀的结构光图像的条纹快速提取方法

doi:10.11896/j.issn.1002-137X.2019.05.042

摘要/Abstract

摘要： 结构光图像的条纹提取易受不均匀光照的影响,而提取的条纹精度是后续三维重建精确与否的重要前提,因此,如何消除不均匀光照的影响,准确提取结构光图像的条纹是本文研究的目标。文中提出了一种结合高斯滤波和均值滤波的处理算法,适用于光照不均匀的结构光图像的条纹提取。该算法既可以有效地消除不均匀光照对图像的影响,又保留了原始图像的特征信息,取得了良好的实验效果。为了加速滤波处理过程,文中使用可分离滤波器对算法进行改进,降低了计算复杂度,又使用基于GPU并行计算的CUDA技术对算法进行加速,使处理速度得到较大的提高。

关键词: CUDA加速, 不均匀光照, 可分离滤波器, 条纹提取

Abstract: The stripe extraction of structured light images can be easily impacted by uneven illumination.The accuracy of the extracted stripes is an important prerequisite for the accuracy of the subsequent 3D reconstruction.Therefore,how to eliminate the influence of uneven illumination and accurately extract the stripes of structured light images is the goal of the study.This paper proposed a processing algorithm combining Gaussian filtering and mean filtering,which is suitable for the structural light image stripe extraction of uneven illumination.The algorithm not only can effectively eliminate the influence of uneven illumination on the image,but also retains the feature information of the original image and achieves good experimental results. In order to speed up the filtering process, this paper used separable filters to improve the algorithm,reducing the computational complexity.In addition,GPU parallel computing-based CUDA technique is used to accelerate the algorithm,and the processing speed is improved greatly.

Key words: CUDA acceleration, Separable filter, Stripe extraction, Uneven illumination

中图分类号:

TP391

郑红波, 石豪, 杜轶诚, 张美玉, 秦绪佳. 光照不均匀的结构光图像的条纹快速提取方法[J]. 计算机科学, 2019, 46(5): 272-278. https://doi.org/10.11896/j.issn.1002-137X.2019.05.042

ZHENG Hong-bo, SHI Hao, DU Yi-cheng, ZHANG Mei-yu, QIN Xu-jia. Fast Stripe Extraction Method for Structured Light Images with Uneven Illumination[J]. Computer Science, 2019, 46(5): 272-278. https://doi.org/10.11896/j.issn.1002-137X.2019.05.042

参考文献

[1]FRIENDL Y M,DENIS D J.Milestones in the History of The-matic Cartography,Statistical Graphics,and Data Visualization.http://www.math.yorku.ca/SCS/Gallery/milestone/2010-03-14.
[2]MCCORMICKB H,DEFANTIT A,BROWN M D.Visualizationin Scientific Computing[J].Computer Graphics,1987,21(6):15-21.
[3]GIACOBBEF W.How a Type II Supernova Explodes[J].Electronic Journal of Theoretical Physics,2005,2(6):30-38.
[4]唐泽圣.三维数据场可视化[M].北京:清华大学出版社,2000.
[5]UDUPA J K,HERMANG T.3D Imaging in Medicine[M].United States:CRC Press,2000.
[6]LU J J,FANG Z H,SUN S L.An Adaptive Weighted Mean Filter[J].Journal of Transcluction Technology,2005,18(4):880-882.(in Chinese)卢京晶,方中华,孙胜利.一种自适应的加权均值滤波器[J].传感技术学报,2005,18(4):880-882.
[7]SONG Q,MA L,CAO J,et al.Image Denoising Based on Mean Filter and Wavelet Transform∥Proceedings of the 4th International Conference on Advanced Information Technology and Sensor Application (AITS).Harbin:IEEE,2015:39-42.
[8]ZHANG L D.Improving Fuzzy Weighted Mean Filter Based on the Degree of Image Noise[J].Computing Technology and Automation,2016,35(2):66-70.(in Chinese)张鲁丹.基于图像受噪程度的改进模糊加权均值滤波[J].计算技术与自动化,2016,35(2):66-70.
[9]SUNT,NEUVOY.Detail-preserving median based filters in ima-ge processing[J].Pattern Recognition Letters,1994,15(4):341-347.
[10]秦前清.实用小波分析[M].西安:西安电子科技大学出版社,1994.
[11]王大凯,侯榆青,彭进业.图像处理的偏微分方程方法[M].北京:科学出版社,2008.
[12]罗述谦,周果宏.医学图像处理与分析[M].北京:科学出版社,2003.
[13]RAFAEL C,GONZALE Z.Digital Image Processing (SecondEdition)[M].Beijing:Publishing House of Electronics Industry,2008.(in Chinese)冈萨雷斯.数字图像处理(第二版)[M].阮秋琦,译.北京:电子工业出版社,2008.
[14]Gaussian blur[M/OL].http://en.wikipedia.org/wiki/Gaus-sian_blur2011-06-25.
[15]Linear separability[M/OL].http://en.wikipedia.org/wiki Linearly_separable2011-06-01.
[16]GARCIA V,DEBREUVE E,BARLAUD M.Fast k nearestneighbor search using GPU∥Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Washington:IEEE,2008:1-6.
[17]SAWANT N,KULKARNI D.Performance Evaluation of Feature Extraction Algorithm on GPGPU∥Proceedings of the 2011 International Conference on Communication Systems and Network Technologies.Washington:IEEE,2011:536-540.
[18]SIMEK V,ASN R R.GPU Acceleration of 2D-DWT Image Compression in MATLAB with CUDA∥Proceedings of the Second UKSIM European Symposium on Computer Modeling and Simulation.Liverpool:IEEE,2008:274-277.
[19]HYOSEOK S,OHYOUNG K.CUDA implementation of Mc-Cann99 retinex algorithm∥Proceedings of the 5th International Conference on Computer Sciences and Convergence Information Technology.Washington:IEEE,2010:388-393.
[20]AFIF M,SAID Y,ATRI M.Efficient implementation of inte-grall image algorithm on NVIDIA CUDA∥Proceedings of the 2018 International Conference on Advanced Systems and Electric Technologies(IC_ASET).Hammamet:IEEE,2018:1-5.
[21]ZENG X J,CHEN Q,TAN H P,et al.CUDA-based Acceleration Algorithm of Bilateral Filtering[J].Computer Science,2015,42(Z6):163-167.(in Chinese)曾炫杰,陈强,谭海鹏,等.基于CUDA的加速双边滤波算法[J].计算机科学,2015,42(Z6):163-167.
[22]WANG Z N,LIU C Z.Implementation of multi-scale Retinex image enhancement algorithm based on CUDA[J].Journal of Computer Applications,2010,30(9):2441-2443.(in Chinese)王正宁,刘昌忠.基于CUDA的多尺度Retinex图像增强算法实现[J].计算机应用,2010,30(9):2441-2443.
[23]LI H,XIE W,WANG X,et al.GPU implementation of multi-scale Retinex image enhancement algorithm∥Proceedings of IEEE/ACS International Conference on Computer Systems and Applications (AICCSA).Agadir:IEEE,2016:1-5.

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