切换滤波方法在深度图像噪声处理中的应用
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摘要
基于Kinect深度图像的互动投影系统受到多种混合噪声干扰.单一的滤波方法只针对特定噪声,往往难以取得很好的效果,通常要将几种滤波方法联合使用.一般的联合滤波虽然可以保证图像的精确度,但算法复杂度高、耗时较长,影响了互动投影系统的实时性.针对上述矛盾,本文首次将切换系统方法引入到数字图像滤波领域,提出了一种任意切换滤波算法,并将其应用到实际的互动感投影系统中.实验结果表明,切换滤波算法既能很好地去除噪声,又能保证跟踪的实时性,达到了良好的目标跟踪效果,可以广泛应用于各种图像滤波处理系统中.
The interactive projection system based on the depth image acquired by Kinect suffers from a variety of noise interferences. A single filtering method is only for specific noise, and it is difficult to obtain good results. Therefore, it is common to combine several filtering methods for filtering the mixed noise. Although the general sum filter can ensure the accuracy of the image, the algorithm has high complexity and takes a long time, which affects the real-time performance of the interactive projection system. In view of the above contradiction, the switching system method is introduced into the field of digital image filtering, an arbitrary switching filter algorithm is proposed, and it is applied on the actual interactive projection system. The experimental results show that the switching filter algorithm can not only remove the noise well, but also guarantee the real-time tracking performance, and achieve a good target tracking effect. The algorithm can be widely used in various image filtering processing systems.
The interactive projection system based on the depth image acquired by Kinect suffers from a variety of noise interferences. A single filtering method is only for specific noise, and it is difficult to obtain good results. Therefore, it is common to combine several filtering methods for filtering the mixed noise. Although the general sum filter can ensure the accuracy of the image, the algorithm has high complexity and takes a long time, which affects the real-time performance of the interactive projection system. In view of the above contradiction, the switching system method is introduced into the field of digital image filtering, an arbitrary switching filter algorithm is proposed, and it is applied on the actual interactive projection system. The experimental results show that the switching filter algorithm can not only remove the noise well, but also guarantee the real-time tracking performance, and achieve a good target tracking effect. The algorithm can be widely used in various image filtering processing systems.
引文
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[2] YU L, LI C, FEI S. Any-wall touch control system with switching filter based on 3D sensor. IEEE Sensors Journal, 2018, 18(11):4697–4703.
[3] GUAN Xinping, ZHAO Lixing, TANG Yinggan. Image denoising and hybrid filtering method. Journal of Image and Graphic, 2005,10(3):332–337.(关新平,赵立兴,唐英干.图像去噪混合滤波方法.中国图象图形学报, 2005, 10(3):332–337.)
[4] XIANG Liling, LIU Zhi, QI Ji, et al. Method image denoising hybrid filtering. Journal of Jilin University(Information Science Edition),2013, 31(3):266–271.(项力领,刘智,齐冀,等.图像去噪的混合滤波方法.吉林大学学报(信息科学版), 2013, 31(3):266–271.)
[5] DAI G J, YU L, XU H N, et al. Federated filter algorithm with positioning technique based on 3D sensor. Circuits Systems&Signal Processing, 2017(11):1–16.
[6] CHENG Daizhan, GUO Yuqian. Switching system progress. Journal of Control Theory&Applications, 2005, 22(6):954–960.(程代展,郭宇骞.切换系统进展.控制理论与应用, 2005, 22(6):954–960.)
[7] ZHAO Jun, NIE Hong. Adequate conditions for switching system input to state stability. IEEE/CAA Journal of Automatica Sinica, 2003,29(2):252–257.(赵军,聂宏.切换系统输入对状态稳定性的充分条件.自动化学报,2003, 29(2):252–257.)
[8] SU X J, SHI P, WU L G, et al. Fault detection filtering for nonlinear switched stochastic systems. IEEE Transactions on Automatic Control, 2016, 61(5):1310–1315.
[9] YU L, ZHANG M, FEI S. Nonlinear adaptive sliding mode switching control with average dwell-time. International Journal of System Science, 2013, 44(3):471–478.
[10] YU L, FEI S, ZHU H, et al. Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign. Journal of Control Theory and Applications, 2011, 9(4):579–583.
[11] ZHANG Xiaoli, LIU Yuzhong, ZHAO Jun. Asymptotic stability of a class of discrete switched systems. Journal of Control Theory&Applications, 2002, 19(5):774–776.(张霄力,刘玉忠,赵军.一类离散切换系统的渐近稳定性.控制理论与应用, 2002, 19(5):774–776.)
[12] DAN G, GROSU A C, ROTENSTEIN E P. Approximate and approximate null-controllability of a class of piecewise linear Markov switch systems. Systems&Control Letters, 2016, 96(10):118–123.
[13] ZHANG Xuming, XU Binshi, DONG Shiyun. Adaptive median filter for image processing. Journal of Computer-Aided Design&Computer Graphics, 2005, 17(2):295–299.)(张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波.计算机辅助设计与图形学学报, 2005, 17(2):295–299.)
[14] CHOI K S, LEE J S, KO S J. New autofocusing technique using the frequency selective weighted median filter for video cameras. IEEE Transactions on Consumer Electronics, 2002, 45(3):820–827.
[15] ROY A, SINGHA J, Manam L, et al. Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Processing, 2017, 11(6):352–361.
[16] TEREJANU G, SINGLA P, SINGH T, et al. Adaptive gaussian sum filter for nonlinear bayesian estimation. IEEE Transactions on Automatic Control, 2011, 56(9):2151–2156.
[17] HUANG J, PAN X, PENG X, et al. Digital image correlation with self-adaptive gaussian windows. Experimental Mechanics, 2013,53(3):505–512.
[18] YU Han, ZHANG Xiujie, CHEN Jianwei, et al. An improved gaussian filtering algorithm considering randomly measured time-delay and synchronous correlation noise. Journal of Control Theory&Applications, 2016, 33(2):133–145.(于浛,张秀杰,陈建伟,等.考虑随机量测时滞和同步相关噪声的改进高斯滤波算法.控制理论与应用, 2016, 33(2):133–145.)
[19] KHAN T M, BAILEY D G, KHAN M. Efficient hardware implementation for fingerprint image enhancement using anisotropic gaussian filter. IEEE Transactions on Image Processing, 2017, 26(5):2116–2126.
[20] XU Guangyu, TAN Jieqing, ZHONG Jinqin. Adaptive effective nonlocal image filtering. Journal of Image and Graphic, 2012, 17(4):471–479.(许光宇,檀结庆,钟金琴.自适应的有效非局部图像滤波.中国图象图形学报, 2012, 17(4):471–479.)
[2] YU L, LI C, FEI S. Any-wall touch control system with switching filter based on 3D sensor. IEEE Sensors Journal, 2018, 18(11):4697–4703.
[3] GUAN Xinping, ZHAO Lixing, TANG Yinggan. Image denoising and hybrid filtering method. Journal of Image and Graphic, 2005,10(3):332–337.(关新平,赵立兴,唐英干.图像去噪混合滤波方法.中国图象图形学报, 2005, 10(3):332–337.)
[4] XIANG Liling, LIU Zhi, QI Ji, et al. Method image denoising hybrid filtering. Journal of Jilin University(Information Science Edition),2013, 31(3):266–271.(项力领,刘智,齐冀,等.图像去噪的混合滤波方法.吉林大学学报(信息科学版), 2013, 31(3):266–271.)
[5] DAI G J, YU L, XU H N, et al. Federated filter algorithm with positioning technique based on 3D sensor. Circuits Systems&Signal Processing, 2017(11):1–16.
[6] CHENG Daizhan, GUO Yuqian. Switching system progress. Journal of Control Theory&Applications, 2005, 22(6):954–960.(程代展,郭宇骞.切换系统进展.控制理论与应用, 2005, 22(6):954–960.)
[7] ZHAO Jun, NIE Hong. Adequate conditions for switching system input to state stability. IEEE/CAA Journal of Automatica Sinica, 2003,29(2):252–257.(赵军,聂宏.切换系统输入对状态稳定性的充分条件.自动化学报,2003, 29(2):252–257.)
[8] SU X J, SHI P, WU L G, et al. Fault detection filtering for nonlinear switched stochastic systems. IEEE Transactions on Automatic Control, 2016, 61(5):1310–1315.
[9] YU L, ZHANG M, FEI S. Nonlinear adaptive sliding mode switching control with average dwell-time. International Journal of System Science, 2013, 44(3):471–478.
[10] YU L, FEI S, ZHU H, et al. Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign. Journal of Control Theory and Applications, 2011, 9(4):579–583.
[11] ZHANG Xiaoli, LIU Yuzhong, ZHAO Jun. Asymptotic stability of a class of discrete switched systems. Journal of Control Theory&Applications, 2002, 19(5):774–776.(张霄力,刘玉忠,赵军.一类离散切换系统的渐近稳定性.控制理论与应用, 2002, 19(5):774–776.)
[12] DAN G, GROSU A C, ROTENSTEIN E P. Approximate and approximate null-controllability of a class of piecewise linear Markov switch systems. Systems&Control Letters, 2016, 96(10):118–123.
[13] ZHANG Xuming, XU Binshi, DONG Shiyun. Adaptive median filter for image processing. Journal of Computer-Aided Design&Computer Graphics, 2005, 17(2):295–299.)(张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波.计算机辅助设计与图形学学报, 2005, 17(2):295–299.)
[14] CHOI K S, LEE J S, KO S J. New autofocusing technique using the frequency selective weighted median filter for video cameras. IEEE Transactions on Consumer Electronics, 2002, 45(3):820–827.
[15] ROY A, SINGHA J, Manam L, et al. Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Processing, 2017, 11(6):352–361.
[16] TEREJANU G, SINGLA P, SINGH T, et al. Adaptive gaussian sum filter for nonlinear bayesian estimation. IEEE Transactions on Automatic Control, 2011, 56(9):2151–2156.
[17] HUANG J, PAN X, PENG X, et al. Digital image correlation with self-adaptive gaussian windows. Experimental Mechanics, 2013,53(3):505–512.
[18] YU Han, ZHANG Xiujie, CHEN Jianwei, et al. An improved gaussian filtering algorithm considering randomly measured time-delay and synchronous correlation noise. Journal of Control Theory&Applications, 2016, 33(2):133–145.(于浛,张秀杰,陈建伟,等.考虑随机量测时滞和同步相关噪声的改进高斯滤波算法.控制理论与应用, 2016, 33(2):133–145.)
[19] KHAN T M, BAILEY D G, KHAN M. Efficient hardware implementation for fingerprint image enhancement using anisotropic gaussian filter. IEEE Transactions on Image Processing, 2017, 26(5):2116–2126.
[20] XU Guangyu, TAN Jieqing, ZHONG Jinqin. Adaptive effective nonlocal image filtering. Journal of Image and Graphic, 2012, 17(4):471–479.(许光宇,檀结庆,钟金琴.自适应的有效非局部图像滤波.中国图象图形学报, 2012, 17(4):471–479.)