基于小波能量融合的显微序列图像合成研究及其在工件表面检测中的应用
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摘要
由于显微镜物镜焦深范围有限，造成显微图像局部模糊，研究人员对图像的理解不完整等问题，本文研究了显微图像融合方法。
首先，通过本文提出的显著边界特征匹配法，对图像进行自动配准，经过分析和实验表明此配准方法能够达到高精度图像融合的要求；其次，分析了小波算法在图像分解与重构中的原理及其融合规则，并在此基础上提出了一种全新的基于能量规则的小波融合序列图像的方法，在实验中表明了小波能量融合的方法能相当好的抑制图像融合失真问题；然后，本文使用计算图像熵的方法对图像清晰度进行评定，效果良好，同时加以均值和标准差两种评价标准对本文所有的融合实验结果进行了综合的评价，避免单一参量的片面性；最后，将此系统成功的应用于刀具检测和工件表面检测。
本文提出了根据提取聚焦部分图像的小波参数，分析聚焦部分和模糊区域在小波变换域内图像能量分布密度的差异，以其高频部分与低频部分的比值为特征参照值，取其最大值为新的小波系数的融合规则。此方法能很好的判断、剔除成像中散焦部分及解决一些融合中的失真问题。在实验中将证明，所提出的方法比常规的基于区域的以像素点取最大值的小波融合方法有明显的优势。由此得出的聚焦合成图像使研究人员可直接测量所需的显微结果。
Due to the limitation of the microscopic focal length, the obtained microscopic images are often blurred in some local areas, which causes that people can not recognize or understand it completely. And this paper researches the fusion method about the microscopy serial images.
Firstly, the paper adopts the edge characteristic image registration, and the practical experiments prove this auto-matching method is effective. Secondly, the research analyses the principle of image decomposition and reconstruction based on wavelet-pyramid method. From the former way the paper proposes a new wavelet transform algorithm based on the energy fusion rules. And the new way can remove the defocused areas showed by the experimental result. And through the entropy and others evaluation, the comprehensive evaluation result was achieved. Thirdly, this research is successfully applied to the examination on the cutting tool surface and workpiece surface.
Based on the different energy distributions of the low-frequency band and the high-frequency band, which are achieved by discrete wavelets transform, an original method for wavelet coefficient combination is proposed in this paper. It takes the ratio of the two bands as a characteristic
首先，通过本文提出的显著边界特征匹配法，对图像进行自动配准，经过分析和实验表明此配准方法能够达到高精度图像融合的要求；其次，分析了小波算法在图像分解与重构中的原理及其融合规则，并在此基础上提出了一种全新的基于能量规则的小波融合序列图像的方法，在实验中表明了小波能量融合的方法能相当好的抑制图像融合失真问题；然后，本文使用计算图像熵的方法对图像清晰度进行评定，效果良好，同时加以均值和标准差两种评价标准对本文所有的融合实验结果进行了综合的评价，避免单一参量的片面性；最后，将此系统成功的应用于刀具检测和工件表面检测。
本文提出了根据提取聚焦部分图像的小波参数，分析聚焦部分和模糊区域在小波变换域内图像能量分布密度的差异，以其高频部分与低频部分的比值为特征参照值，取其最大值为新的小波系数的融合规则。此方法能很好的判断、剔除成像中散焦部分及解决一些融合中的失真问题。在实验中将证明，所提出的方法比常规的基于区域的以像素点取最大值的小波融合方法有明显的优势。由此得出的聚焦合成图像使研究人员可直接测量所需的显微结果。
Due to the limitation of the microscopic focal length, the obtained microscopic images are often blurred in some local areas, which causes that people can not recognize or understand it completely. And this paper researches the fusion method about the microscopy serial images.
Firstly, the paper adopts the edge characteristic image registration, and the practical experiments prove this auto-matching method is effective. Secondly, the research analyses the principle of image decomposition and reconstruction based on wavelet-pyramid method. From the former way the paper proposes a new wavelet transform algorithm based on the energy fusion rules. And the new way can remove the defocused areas showed by the experimental result. And through the entropy and others evaluation, the comprehensive evaluation result was achieved. Thirdly, this research is successfully applied to the examination on the cutting tool surface and workpiece surface.
Based on the different energy distributions of the low-frequency band and the high-frequency band, which are achieved by discrete wavelets transform, an original method for wavelet coefficient combination is proposed in this paper. It takes the ratio of the two bands as a characteristic
引文
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[3] 瞿继双，王超，王正志，基于数据融合的遥感图像处理技术[J]，中国图像图形学报，2002，vol．7，985-993
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[9] Akerman A. Pyramidal techniques for multi-sensor fusion [J]. Proc SPIE, 1992, 1828: 124-131.
[10] Y.Chibani, A. Houacine, Redundant versus orthogonal wavelet decomposition for multisensor image fusion [J], Pattern Recognition, 2003, vol.36: 1785-1794.
[11] Gonzalo Pajares, Jesus Manuel de la Cruz, A wavelet-based image fusion tutorial [J], Pattern Recognition, 2004, vol.37:1855-1872.
[12] G. Piella, A general framework for multi-resolution image fusion: from pixels to regions [J], Information Fusion, 2003, vol.4: 259-280.
[13] 刘维一，王肇圻，母国光，方志良，用空间图像融合技术进行三维重建的研究[J]，光学学报，2000，20，363-366
[14] 韩红，刘允才，韩崇昭等，多传感器融合多目标跟踪中的序贯航迹关联算法[J]，2004，vol．20，30-34
[15] 王婷，赵鸣，李杰，图像融合在探测混凝土结构内部损伤中的应用[J]，计算机工程与应用，2005，vol．26，187-190．
[16] Wenzhong Shi, ChangQing Zhu, Yan Tian and Janet Nichol, Wavelet-based image fusion and quality assessment[J]. International Journal of Applied Earth Observation and Geoinformation, Volume 6, Issues 3-4, March 2005, Pages 241-251
[17] 姜志国等，聚焦合成的显微三维成像系统[J]，CT理论与应用研究，2004，vol．13，no．4，9-15
[18] 吕遐东，黄心汉，王敏等，基于显微图像散焦特征的微操作机器人深度信息提取[J]，机器人，2003，vol．25：322-326
[19] 马莉，黄敏，一种基于多分辨率与模糊聚类技术的散焦图像分割算法[J]，中国图象图 形学报，2005，vol．10：290-294
[20] 阎祥福，高岳，苏学刚，魏建中，王仲春，一种多重图像融合系统的电配准技术[J]，光学技术，1999(3)，14-16
[21] 马慧军等，色素痣三维结构的实现与研究[J]，CT理论与应用研究，2002，vol．11，no．1，pp．26-29，
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[23] R.J. Althof, M.G.J. Wind, J.T. Dobbins, A rapid and automatic image registration algorithm with sub pixel accuracy [J], IEEE Trans. Med. Imaging vol.16 (3) (1997) 308 - 316.
[24] A. Rosendfeld, M. Thurston, Edge and curve detection for visual scene analysis [J], IEEE Trans. Comput. vol.20 (1971) 562 - 569.
[25] 母国光，战元龄．光学[M]．北京：高等教育出版社，1987
[26] 孙立宁，陈立国，刘品宽，荣伟斌，微操作机器人显微视觉系统若干问题[J]，光学精密过程，2002，vol．10(2)，171-175
[27] Pentland A P. A new sense for depth of field [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 1987, vol.9 (7): 523-531.
[28] Ens J, Lawrence P. An investigation of methods for determing depth from focus [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 1993, vol.15 (2): 97-107.
[29] Xiong Y, Sharer S A. Depth from focusing and defocusing [A]. Proc. IEEE Conf. Computer Vision and Pattem Recognition[C], 1993, New York: 68- 73.
[30] Djemel Zioul and Francois Deschenes, Depth from Defocus Estimation in Spatial Domain [J], Computer Vision and Image Understanding, vol.81, pp.143-165, 2001.
[31] 李勤，单细胞微弱荧光图像探测及其融合处理研究，博士学位论文，北京理工大学，1998
[32] Z. Zhang, R.S. Blum, A categorization ofmultiscale decomposition-based image fusion schemes with a performance study for a digital camera application[J], Proc. IEEE 87 (8) (1999) 1315-1326
[33] T. Ranchin, L. Wald, Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation [J], Photogram. Eng. Remote Sensing vol.66 (1) (2000) 49-61.
[34] H. Li, B.S. Manjunath, S.K. Mitra, Multisensor image fusion using the wavelet transform [J], Graphical Models Image Process. vol.57 (3) (1995) 235 - 245.
[35] D. Marr, Vision, Freeman [M], San Francisco, CA, 1982.
[36] P.J. Burr, E. Adelson, The Laplacian pyramid as a compact image code [J], IEEE Trans. Comput. vol.31 (1983) 532-540.
[37] J. Kautsky, J. Flnsser, B. Zitova, S. Simberova, A new wavelet-based measure of image focus [J], Pattern Recognition Letters, vol.23, pp. 1785-1794, 2002.
[38] T. Lindeberg, Scale-Space Theory in Computer Vision, Kluwer, Norwell, MA, 1994.
[39] B. Garguet-Duport, J. Girel, J. Chassery, J.G. Pautou, The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data [J], Photogram. Eng. Remote Sensing vol.62 (9) (1996) 1057-1066.
[40] C. Schmid, R. Mohr, C. Bauckhage, Evaluation of interest points [J], Int. J. Comput. Vision 37 (2) (2000) 151-172.
[41] E.J. Stollnitz, T.D. DeRose, D.H. Salesin, Wavelets for computer graphics: a primer, part 1[J], IEEE Comput. Graphics Appl. vol.15 (3) (1995) 76-84.
[42] G. Piella, A region-based multi-resolution image fusion algorithm [J], ISIF Fusion 2002 conference, Annapolis, July, 2002.
[43] 陈晓钟，孙华燕，基于能量特征的图像目标检测[J]，红外与激光工程，vol．30，no．1，pp．30-36，Feb．2001．
[44] E.H. Adelson, C.H. Anderson, J.R. Bergen, P.J. Butt, J. Ogden, Pyramid methods in image processing [J], RCA Eng. vol.29 (6) (1984) 33-41.
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