首页 > 论文 > 激光与光电子学进展 > 57卷 > 2期(pp:21003--1)

基于剪切波变换的改进全变分散斑去噪方法

Shearlet-Transform-Based Improved Total Variation Speckle Denoising Method

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

在散斑去噪过程中保持图像边缘纹理特征,是光学相干层析图像处理技术的难题。散斑去噪过程中的散斑残留和边缘纹理模糊是该难题的主要诱导因素。为解决这一难题,提出一种基于剪切波变换的改进全变分散斑去噪方法。该方法结合剪切波变换和传统全变分模型,对不同图像区域采用针对性的去噪策略,兼顾散斑去噪与纹理保留,提高了光学相干层析图像的噪声抑制效果。对不同生理、病理状态下的视网膜光学相干层析图像进行测试,结果表明:该方法通过采用区域针对性策略改进了噪声抑制能力,通过引入剪切波变换方法提高了边缘纹理保持能力,进而同时实现散斑去除和纹理保留。此外,与其他散斑去噪方法进行对比,验证了该方法的有效性。

Abstract

In the field of optical coherence tomography, reducing the speckle noise while protecting the textural features of image edge is difficult mainly because of the speckle residue and textural blur of edge in the speckle denoising process. To solve this problem, this study proposes a shearlet-transform-based improved total variation speckle denoising method. By combining the shearlet transform with the traditional total variation model, as well as a targeted denoising strategy applied on different image regions, the proposed method reduces the speckle noise without disturbing the texture in the image, and further improves the speckle-noise suppression in the original optical coherence tomography image. The proposed method is tested on many retinal optical coherence tomography images under different physiological and pathological conditions. Results show that the regional targeted strategy in the proposed method improves the ability of speckle-noise suppression, while the shearlet transform improves the ability of the edge texture protection, resulting in simultaneous speckle reduction and texture protection. The effectiveness of the proposed method is also confirmed in comparison with other common speckle denoising methods.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:T391.4

DOI:10.3788/LOP57.021003

所属栏目:图像处理

基金项目:国家自然科学基金;

收稿日期:2019-05-27

修改稿日期:2019-06-26

网络出版日期:2020-01-01

作者单位    点击查看

邱岳:天津大学电气自动化与信息工程学院, 天津 300072
唐晨:天津大学电气自动化与信息工程学院, 天津 300072
徐敏:天津大学电气自动化与信息工程学院, 天津 300072
黄圣鉴:天津大学电气自动化与信息工程学院, 天津 300072
雷振坤:大连理工大学工业装备结构分析国家重点实验室, 辽宁 大连116023

联系人作者:唐晨(tangchen@tju.edu.cn)

备注:国家自然科学基金;

【1】Huang D, Swanson E A, Lin C P, et al. Optical coherence tomography [J]. Science. 1991, 254(5035): 1178-1181.

【2】Drexler W, Morgner U, Ghanta R K, et al. Ultrahigh-resolution ophthalmic optical coherence tomography [J]. Nature Medicine. 2001, 7(4): 502-507.

【3】Swanson E A. Optical coherence tomography: principles, instrumentations, and applications . [C]∥Technical Digest. Summaries of papers presented at the Conference on Lasers and Electro-Optics. Postconference Edition. CLEO''''99. Conference on Lasers and Electro-Optics (IEEE Cat. No. 99CH37013), May 28-28, 1999, Baltimore, MD, USA. New York: IEEE. 1999, 312.

【4】Chen J B, Zeng Y G, Yuan Z L, et al. Optical coherence tomography based on dynamic speckle [J]. Acta Optica Sinica. 2018, 38(1): 0111001.
陈俊波, 曾亚光, 袁治灵, 等. 基于动态散斑的光学相干层析成像技术 [J]. 光学学报. 2018, 38(1): 0111001.

【5】Schmitt J M, Xiang S H, Yung K M. Speckle in optical coherence tomography [J]. Journal of Biomedical Optics. 1999, 4(1): 95-105.

【6】Yu Y J, Acton S T. Speckle reducing anisotropic diffusion [J]. IEEE Transactions on Image Processing. 2002, 11(11): 1260-1270.

【7】Salinas H M, Fernandez D C. Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography [J]. IEEE Transactions on Medical Imaging. 2007, 26(6): 761-771.

【8】Bernardes R, Maduro C, Serranho P, et al. Improved adaptive complex diffusion despeckling filter [J]. Optics Express. 2010, 18(23): 24048-24059.

【9】Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms [J]. Physica D: Nonlinear Phenomena. 1992, 60: 259-268.

【10】Yuan Z L, Chen J B, Huang W Y, et al. Speckle noise reduction of optical coherence tomography based on robust principle component analysis algorithm [J]. Acta Optica Sinica. 2018, 38(5): 0511002.
袁治灵, 陈俊波, 黄伟源, 等. 基于稳健性主成分分析算法的光学相干层析成像去除散斑噪声的研究 [J]. 光学学报. 2018, 38(5): 0511002.

【11】Deng J X, Liang Y M. Noise reduction with wavelet transform in optical coherence tomographic images [J]. Acta Optica Sinica. 2009, 29(8): 2138-2141.
邓菊香, 梁艳梅. 光学相干层析图像的小波去噪方法研究 [J]. 光学学报. 2009, 29(8): 2138-2141.

【12】Adler D C, Ko T H, Fujimoto J G. Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter [J]. Optics Letters. 2004, 29(24): 2878-2880.

【13】Jian Z P, Yu Z X, Yu L F, et al. Speckle attenuation in optical coherence tomography by curvelet shrinkage [J]. Optics Letters. 2009, 34(10): 1516-1518.

【14】Guo Q, Dong F M, Sun S F, et al. Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image [J]. IET Image Processing. 2013, 7(5): 442-450.

【15】Easley G, Labate D, Lim W Q. Sparse directional image representations using the discrete shearlet transform [J]. Applied and Computational Harmonic Analysis. 2008, 25(1): 25-46.

【16】Lim W Q. The discrete shearlet transform: a new directional transform and compactly supported shearlet frames [J]. IEEE Transactions on Image Processing. 2010, 19(5): 1166-1180.

【17】Chen Y, Li Z L, Nan N, et al. Speckle noise reduction in Fourier domain polarization-sensitive coherence tomography by split-spectrum [J]. Acta Optica Sinica. 2018, 38(8): 0811004.
陈艳, 李中梁, 南楠, 等. 偏振频域光学相干层析成像中散斑噪声降低方法 [J]. 光学学报. 2018, 38(8): 0811004.

【18】Bezdek J C, Ehrlich R, Full W. FCM: the fuzzy c-means clustering algorithm [J]. Computers & Geosciences. 1984, 10(2/3): 191-203.

【19】Goldstein T, Osher S. The split Bregman method for L1-regularized problems [J]. SIAM Journal on Imaging Sciences. 2009, 2(2): 323-343.

【20】Lu B B, Wang L R, Wang Y M, et al. Anisotropic total variation guided filtering and its Split Bregman algorithm [J]. Laser & Optoelectronics Progress. 2017, 54(5): 051005.
芦碧波, 王乐蓉, 王永茂, 等. 各向异性全变分引导滤波及其Split Bregman方法 [J]. 激光与光电子学进展. 2017, 54(5): 051005.

【21】Kermany D S, Goldbaum M, Cai W J, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning [J]. Cell. 2018, 172(5): 1122-1131.

引用该论文

Qiu Yue,Tang Chen,Xu Min,Huang Shengjian,Lei Zhenkun. Shearlet-Transform-Based Improved Total Variation Speckle Denoising Method[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021003

邱岳,唐晨,徐敏,黄圣鉴,雷振坤. 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): 021003

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF

欧洲女人性开放视频