基于GHM多小波变换的非织造布多焦面图像融合

doi:10.13475/j.fzxb.20180505008

纺织学报 ›› 2019, Vol. 40 ›› Issue (06): 125-132.doi: 10.13475/j.fzxb.20180505008

基于GHM多小波变换的非织造布多焦面图像融合

陈阳¹, 辛斌杰²(), 邓娜¹

1.上海工程技术大学电子电气工程学院, 上海 201620
2.上海工程技术大学服装学院, 上海 201620

收稿日期:2018-05-21 修回日期:2019-02-21 出版日期:2019-06-15 发布日期:2019-06-25
通讯作者: 辛斌杰
作者简介:陈阳(1995—),女,硕士生。主要研究方向为数字图像处理。
基金资助:
上海市自然科学基金项目(18ZR1416600)

Nonwovens multi-focus fusion based on GHM multi-wavelet transform

CHEN Yang¹, XIN Binjie²(), DENG Na¹

1. College of Electrical Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2. College of Fashion, Shanghai University of Engineering Science, Shanghai 201620, China

Received:2018-05-21 Revised:2019-02-21 Online:2019-06-15 Published:2019-06-25
Contact: XIN Binjie

摘要/Abstract

摘要：

针对光学显微镜在单一焦平面下拍摄的织物图像部分区域纤维会模糊的问题,提出基于GHM多小波变换的非织造布多焦面图像融合算法。利用自行搭建的非织造布显微成像系统采集不同焦平面下的织物图像序列,对初始图像序列进行临界采样预滤波处理,使用2种融合方法逐一处理图像的高低频,初始织物融合图像经多小波融合及逆变换后获得,之后按上述方法将初始融合图像与后续单焦面图像融合,叠加循环至融合后所有纤维区域均能清晰显示为止结束收敛。实验结果表明,该融合方法能将不同焦平面下拍摄的图像序列进行数字化图像融合,达到单幅图像内全视野区域的纤维网清晰聚焦融合的效果,为之后的计算机图像处理及测量提供便利。

关键词: 临界采样预滤波, GHM多小波, 多焦面融合, 非织造布图像, 显微成像

Abstract:

Aiming at the problem that the fibers in some areas of the fabric image captured by the optical microscope under a single focal plane will be blurred, a multi-focus image fusion algorithm based on multi-wavelet transform was proposed. Using the self-built nonwoven fabric microscopic imaging system to acquire the fabric image sequences under different focal planes, the initial image sequence was subjected to critical sampling pre-filtering, and the two fusion methods were used to process the high and low frequencies of the images one by one. The original fabric fusion image was after wavelet fusion and inverse transformation, the original fusion image and the subsequent single-focus surface image were merged according to the above method. After the integration cycle was completed, all the fiber regions could be clearly displayed until the convergence was completed. Experiments show that the fusion method can digitally image the image sequence taken under different focus planes, achieve the effect of clear focusing and fusion of the fiber mesh in the full field of view within a single image, and provide the foundation for the computer image processing and measurement laying algorithm.

Key words: critical sampling prefiltering, GHM multi-wavelet, multi-focus image fusion, nonwoven image, microscopic imaging

中图分类号:

TP311.1

陈阳, 辛斌杰, 邓娜. 基于GHM多小波变换的非织造布多焦面图像融合[J]. 纺织学报, 2019, 40(06): 125-132.

CHEN Yang, XIN Binjie, DENG Na. Nonwovens multi-focus fusion based on GHM multi-wavelet transform[J]. Journal of Textile Research, 2019, 40(06): 125-132.

图/表 6

图1

图2

图3

图4

图5

表1

融合图像质量评估表"

类型
多小波变换	6.618 6	8.011 7	0.532 2	44.755 0	31.876 0
加权平均	6.458 5	1.783 4	0.861 5	48.865 3	31.843 8
单小波变换	6.367 7	4.751 7	0.787 8	52.281 5	31.198 0
双小波变换	6.336 3	7.954 3	0.681 5	55.529 7	30.771 0
拉普拉斯金字塔融合	5.632 1	7.926 5	0.543 8	58.908 4	30.757 1
对比度金字塔融合	4.409 8	7.944 3	0.564 5	79.210 3	29.284 4
梯度金字塔融合	6.862 6	3.778 2	0.629 4	57.966 0	30.511 0

表1

参考文献 22

[1]	WANG R W, XU B. Multi-focus image fusion for accurate measurement of nonwoven structures[J]. Journal of Industrial Textiles, 2016,46(3):968-985.
[2]	LI X, WANG M. Research of multi-focus image fusion algorithm based on Gabor filter bank[C]//2014 12th International Conference on Signal Processing (ICSP). Hangzhou: IEEE, 2014: 693-697.
[3]	BENES R, DVORAK P, FAUNDEZ-ZANUY M, et al. Multi-focus thermal image fusion[J]. Pattern Recognition Letters, 2013,34(5):536-544.
[4]	POHL C, GENDEREN J L V. Review article multisensor image fusion in remote sensing: concepts, methods and applications[J]. International Journal of Remote Sensing, 1998,19(5):823-854.
[5]	TIAN J, CHEN L, MA L, et al. Multi-focus image fusion using a bilateral gradient-based sharpness criterion[J]. Optics Communications, 2011,284(1):80-87.
[6]	LI H, MANJUNATH B S, MITRA S K. Multisensor image fusion using the wavelet transform[J]. Graphical Models and Image Processing, 1995,57(3):235-245.
[7]	TESCHER A G. Applications of digital image processing XII[J]. Journal of Modern Optics, 1989,26(8):160.
[8]	GOUTSIAS J, HEIJMANS H J A M. Nonlinear multiresolution signal decomposition schemes: I: Morphological pyramids[J]. IEEE Transactions on Image Processing, 2000,9(11):1862-1876. pmid: 18262923
[9]	苗启广, 王宝树. 基于改进的拉普拉斯金字塔变换的图像融合方法[J]. 光学学报, 2007,27(9):1605-1610.
	MIAO Qiguang, WANG Baoshu. Multi-sensor image fusion based on improved laplacian pyramid tran-sform[J]. Acta Optica Sinica, 2007,27(9):1605-1610.
[10]	AKERMAN A. Pyramidal techniques for multi-sensor fusion[J]. Proceedings of SPIE, DOI: 10.1117/12.131644. doi: 10.1117/12.2547015 pmid: 32665745
[11]	ROCKINGER O. Image sequence fusion using a shift-invariant wavelet transform [C]//Proceedings of International Conference on Image Processing. California: IEEE Computer Society, 1997: 288-291.
[12]	MA X, PENG L, XU H. Block-based assimilation of spatial frequency multi-focus image fusion algorithm[J]. Science Technology and Engineering, 2012,12(1):64-67.
[13]	CAO L, JIN L, TAO H, et al. Multi-focus image fusion based on spatial frequency in discrete cosine transform domain[J]. IEEE Signal Processing Letters, 2015,22(2):220-224.
[14]	HAGHIGHAT M B A, AGHAGOLZADEH A, SEYEDARABI H. Multi-focus image fusion for visual sensor networks in DCT domain[J]. Computers & Electrical Engineering, 2011,37(5):789-797.
[15]	楼建强, 李俊峰, 戴文战. 非下采样剪切波变换的医学图像融合[J]. 中国图象图形学报, 2017,22(11):1574-1583.
	LOU Jianqiang, LI Junfeng, DAI Wenzhan. Medical image fusion using nonsubsampled shearlet trans-form[J]. Jouanal of Image and Oraphics, 2017,22(11):1574-1583.
[16]	WANG R W, XU B, ZENG P, et al. Multi-focus image fusion for enhancing fiber microscopic images[J]. Textile Research Journal, 2012,82(4):352-361. doi: 10.1177/0040517511407377
[17]	WANG R, XU B, LI C. Accurate fiber orientation measurements in nonwovens using a multi-focus image fusion technique[J]. Textile Research Journal, 2014,84(2):115-124.
[18]	XU X, WANG R W. A Study on Multi-focus images fusion technology based on fiber boundaries[J]. Advannaturalced Materials Research, 2014,900:547-553.
[19]	杨晓蕊, 葛广英, 朱荣华, 等. 基于小波变换的卫星遥感图像融合与分割[J]. 聊城大学学报(自然科学版), 2018,31(4):18-26.
	YANG Xiaorui, GE Guangying, ZHU Ronghua, et al. Remote sensing image processing of meteorological satellite[J]. Journal of Liaocheng Univesity(Natural Science Edition), 2018,31(4):18-26.
[20]	WANG H H. A new multiwavelet-based approach to image fusion[J]. Journal of Mathematical Imaging & Vision, 2004,21(2):177-192.
[21]	宁梓淯, 李红岩, 李萌. 基于GHM多小波的多模态医学图像融合算法研究[J]. 电子测试, 2013(15):49-50.
	NING Ziyu, LI Hongyan, LI Meng. The study of multi-modality medical image fusion algorithm based on GHM multiwavelet[J]. Electronic Test, 2013(15):49-50.
[22]	HELLER P, STRANG G, TOPIWALA P, et al. The application of multiwavelet filter banks to signal and image processing[J]. IEEE Trans Image Process, 1999,8:548-563. doi: 10.1109/83.753742 pmid: 18262898

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基于GHM多小波变换的非织造布多焦面图像融合

Nonwovens multi-focus fusion based on GHM multi-wavelet transform

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