A New Denoising Approach for Optical Coherence Tomography Image Based on the Total Variational Model

Abstract:

The traditional total variational (TV) model performs well for most noise image. However, the method will lose some information and details for the image which has rich texture and tiny boundary. Therefore, according to the requirements of the OCT pearl image, a novel denoising approach based on the TV model is proposed in this paper. This method combined the adaptive image denoising model and the novel fidelity term. Numerical experiments show that the proposed method can remove the noise while preserving significant image details. At pearl OCT image the method achieves at least 0.1dB gain over other existing denoising methods for Signal-Noise Ratio (SNR) measurement and Peak Signal-Noise Ratio (PSNR) measurement.

• Periodical: Applied Mechanics and Materials (Volumes 278-280)
• Pages: 1383-1387
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.278-280.1383
• Online since: January 2013
• Authors: Jing Liu, Xiao Lin Tian, Yan Kui Sun
• Keywords: Image De-Noising, OCT Image, Total Variational Model

References

[1] Xue Ping, Chen Zemin. The new technology of OCT[J]. Physics and Engineering, 2001, 11(3): 39～43.

[2] Jiang Fagang, Fu Zheqiang, Zhang Ping. Research on Optical Coherence Tomography for Eyes Detection[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2004, 2(2): 53～56.

[3] Yan Chunsheng, Zeng Nan, Lai Shurong. Optical tomography technology research dynamic [J]. Laser Jounal, 2001, 22(5): 5-8.

[4] Wang Xinyu, Zhang Chunping, ZhangLianshun, Optical coherence tomography technology and its application in the biological medicine [J]. Biomedical Optics, 2001, : 27-31.

[5] D. Huang, E. A. Swanson, C. P. Lin, J. S. Schumann, W. G. Stinson, M. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, Optical coherence tomography, Science, vol. 254, p.1178–1181, (1991).

DOI: 10.1126/science.1957169

[6] N. Iftimia, B. E. Bouma, and G. J. Tearney, Speckle reduction in optical coherence tomography by path length encoded angular compounding, J. Biomed. Optics, vol. 8, p.260–260, (2003).

DOI: 10.1117/1.1559060

[7] M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, Speckle reduction in optical coherence tomography by frequency compounding, J. Biomed. Optics, vol. 8, p.565–565, (2003).

DOI: 10.1117/1.1578087

[8] Rudin L 1Osher SFatemi E Nonlinear total variation noise removal algorithms[J]．Physiea D·1992, 6：259-268.

[9] Auber GVeseLA variantal method in image recovery[J]．SlAM Journal of Numerical Analysis, 1997, 34(5)：1948-(1954).

[10] ZHANG Hong-ying, PENG Qi-zong. PDE deduction and its numerical realization in variational image restoration[J]. Computer Engineering & Science, 2006, 28(6): 1007-130X.

[11] Vese L AOshers J．Modeling textuTes with tolal variation minimization and oscillating palterns in image processing[J]．Journal of Scientific computing200319(3)：553—572.

[12] Chen Lixia. Improve the total variational denoising algorithm [J]. Journal of photon. 2009. 38 (4) 1001 100.

[13] ZHANG Hong-ying, PENG Qi-cong. Adaptive image de noising model based on total variation[J]. Opto-Electronic Engineering, 2006, 33(3): 50-53.

[14] Wang Chao, Ye Zhongfu. Variational enhancement for infrared images[J]. Journal of Infrared and Millimeter Waves. 2006 25(4): 306-310.