基于正余弦分解的两分段自适应非局部均值滤波方法

doi:10.16180/j.cnki.issn1007-7820.2024.07.011

摘要/Abstract

摘要：

为解决包裹相位图中存留的散斑噪声问题,文中提出了一种基于正余弦分解的两分段自适应非局部均值滤波方法。该方法通过两次改进衰减参数的大小和相似性度量的方式实现了算法的自适应化。利用该方法对包裹相位图的正余弦分量去噪,去噪后利用反正切运算获取干净的包裹相位,对该相位进行解包裹运算。实验和仿真结果表明,所提方法既有效去除了包裹相位图中的噪声,也保留了相位图中的边缘信息。相比于分别使用SCA(Sine Cosine Algorithm)方法和BM3D(Block-Matching and 3D filtering)方法,通过所提方法去噪后的图像等效视数(Equivalent Number of Looks,ENL)最大,散斑抑制指数(Speckle Suppression Index,SSI)最小,且均方误差提升了约两倍,说明所提方法有效去除了包裹相位中的噪声,提高了相位解包裹的精度。

关键词: 包裹相位, 散斑噪声, 正余弦分解, 两分段, 相似性度量, 自适应化, 非局部均值, 相位解包裹

Abstract:

In order to solve the problem of speckle noise in the wrapped phase diagram, a two-stage adaptive non-local mean filtering method based on sine and cosine decomposition is proposed. The proposed method realizes the adaption of the algorithm by improving the size and similarity measurement of the attenuation parameters twice. This method is used to denoise the sine and cosine components of the wrapped phase diagram. After denoising, the inverse tangent operation is used to obtain the clean wrapped phase, and unwrapping operation is carried out on the phase. The experimental and simulation results show that the proposed method not only effectively removes the noise in the wrapped phase diagram, but also preserves the edge information in the phase diagram. Compared with SCA(Sine Cosine Algorithm) method and BM3D(Block-Matching and 3D filtering) method, ENL(Equivalent Number of Looks) and SSI(Speckle Suppression Index) of the image denoised by the proposed method are the largest and the smallest, and the mean square error is increased by about two times. These results reveal that the proposed method can effectively remove the noise in the wrapped phase and improve the accuracy of phase unwrapping.

Key words: wrapped phase, speckle noise, sine cosine decomposition, two sections, similarity measure, adaptive, non-local mean, phase unwrapping

中图分类号:

O439

孙玉娟, 王亚伟, 汤馥睿, 耿芫, 李雨晨, 徐媛媛. 基于正余弦分解的两分段自适应非局部均值滤波方法[J]. 电子科技, 2024, 37(7): 81-88.

SUN Yujuan, WANG Yawei, TANG Furui, GENG Yan, LI Yuchen, XU Yuanyuan. A Two-Stage Adaptive Non-Local Mean Filtering Method Based on Sine-Cosine Decomposition[J]. Electronic Science and Technology, 2024, 37(7): 81-88.

图/表 13

图1

图2

图3

图4

图5

图6

图7

图8

表1

图9

图10

图11

表2

参考文献 20

[1]	Masters B R. Quantitative phase imaging of cells and tissues[J]. Journal of Biomedical Optics, 2012, 17(2):385-400.
[2]	Popescu G, Park Y, Lue N, et al. Optical imaging of cellmass and growth dynamics[J]. American Journal of Physiology Cell Physiology, 2008, 295(2):538-544.
[3]	Khadir S, Bon P, Vignaud D, et al. Optical imaging and characterization of graphene and other 2D materials using quantitative phase microscopy[J]. ACS Photonics, 2017, 4(12):3130-3139.
[4]	Bu M, Jin W F. Simulation methods on optical thickness of irregular cell based on quantitative phase imaging technique[C]. Nanjing: Global Conference on Biological Engineering and Biomedical, 2015:101-106.
[5]	牛瑞, 田爱玲, 王大森, 等. 数字全息测量系统的散斑噪声抑制[J]. 激光与光电子学进展, 2022, 59(16):86-92.
	Niu Rui, Tian Ailing, Wang Dasen, et al. Speckle noise suppression of digital holography measuring system[J]. Laser and Optoelectronics Progress, 2022, 59(16):86-92.
[6]	肖启阳, 李健, 吴思进, 等. 基于能量的DSPI相位图正余弦降噪[J]. 光学精密工程, 2018, 26(4):936-943.
	Xiao Qiyang, Li Jian, Wu Sijin, et al. Denoising of DSP Iphase map using sinecosine filtering and signal energy[J]. Optics and Precision Engineering, 2018, 26(4):936-943.
[7]	邱岳, 唐晨, 徐敏, 等. 基于剪切波变换的改进全变分散斑去噪方法[J]. 激光与光电子学进展, 2020, 57(2):74-82.
	Qiu Yue, Tang Chen, Xu Min, et al. Shearlet-transform-based improved total variation speckle denoising method[J]. Laser and Optoelectronics Progress, 2020, 57(2):74-82.
[8]	刘吉, 黄晓慧, 武锦辉, 等. 基于正余弦分解的自适应全变分散斑去噪方法[J]. 中国激光, 2020, 47(10):172-179.
	Liu Ji, Huang Xiaohui, Wu Jinhui, et al. Adaptive total variation speckle denoising method based on sine-cosine decomposition[J]. Chinese Journal of Lasers, 2020, 47(10):172-179.
[9]	林薇, 崔海华, 郑炜, 等. 基于深度学习的剪切散斑干涉条纹图滤波方法[J]. 激光与光电子学进展, 2022, 59(22):155-164.
	Lin Wei, Cui Haihua, Zheng Wei, et al. Phase fringe patterns filtering method for shearography using deep learning[J]. Laser and Optoelectronics Progress, 2022, 59(22):155-164.
[10]	Aebischer H A, Waldner S. A simple and effective method for filtering speckle interferometric phase fringe patterns[J]. Optics Communications, 1999, 162(4-6):205-210.
[11]	王智鑫, 胡烁陶, 夏振涛, 等. 基于正余弦变换和BM3D滤波的DSPI图像去噪方法[J]. 自动化与仪器仪表, 2021, 6(7):1-6.
	Wang Zhixin, Hu Shuotao, Xia Zhentao, et al. DSPI image denoising method based on sinecosine decomposition and BM3D filtering[J]. Automation and Instrumentation, 2021, 6(7):1-6.
[12]	蒋汉阳, 戴美玲, 苏志龙, 等. 基于散斑相位条纹方向的自适应正弦/余弦滤波[J]. 光学学报, 2017, 37(9):81-88.
	Jiang Hanyang, Dai Meiling, Su Zhilong, et al. An adaptive sine/cosine filtering algorithm based on speckle phase fringe orientation[J]. Acta Optica Sinica, 2017, 37(9):81-88.
[13]	Xu W J, Tang C, Xu M, et al. Fuzzy cmeans clustering based segmentation and the filtering method for discontinuous ESPI fringe patterns[J]. Applied Optics, 2019, 58(6):1442-1450.
[14]	Thaipanich T, Oh B T, Wu P H, et al. Improved image denoising with adaptive nonlocal means algorithm[J]. IEEE Transactions on Consumer Electronics, 2010, 56(4):2623-2630.
[15]	冷凯群. 基于非局部均值的图像去噪算法研究[D]. 北京: 北京工业大学, 2019:88-93.
	Leng Kaiqun. Research on image denoising algorithm based on nonlocal mean[D]. Beijing: Beijing Universityof Technology, 2019:88-93.
[16]	Devapal D, Kumar S S, Jojy C. A noval approach of despeckling SAR images using nonlocal means filtering[J]. Journal of the Indian Society of Remote Sensing, 2017, 45(3):443-450.
[17]	Chen K, Chen L, Xiao J Q, et al. Speckle reduction in digital holography with nonlocal means filter based on the Pearson correlation coefficient and Butterworth filter[J]. Optics Letters, 2022, 47(2):397-400. doi: 10.1364/OL.444769 pmid: 35030615
[18]	韩豪. 非正交相位成像下的血细胞相位恢复及三维形态重建技术研究[D]. 镇江: 江苏大学, 2021:56-62.
	Han Hao. Phase retrieval method and 3D morphological reconstruction technology of blood cells based on non-orthogonal phase imaging[D]. Zhenjiang: Jiangsu University, 2021:56-62.
[19]	金鑫, 张景雄. 自适应加权中值滤波的InSAR干涉图去噪方法[J]. 测绘地理信息, 2016, 41(3):12-15.
	Jin Xin, Zhang Jingxiong. A selfadaptive weighted-median filter for In SAR interferograms[J]. Journal of Geomatics, 2016, 41(3):12-15.
[20]	Feng X F, Guo X Y, Huang Q H. Systematic evaluationon speckle suppression methods in examination of ultrasound breast images[J]. Applied Sciences-Basel, 2017, 7(1):37-50.

滤波方法	平面波	复杂面波
SCA	0.067 9	0.080 0
BM3D	0.056 4	0.074 8
TSNLM	0.035 9	0.062 9

评价指标	SCA	BM3D	TSNLM
ENL	12.555 3	13.330 1	13.829 6
SSI	0.282 2	0.274 0	0.268 9