Infrared image denoising based on improved threshold and inter-scale correlations of wavelet transform

被引：14
|
作者：
Yang H.-X. [1 ]
Wang X.-S. [1 ]
Xie P.-H. [1 ]
Leng A.-L. [2 ]
Peng Y. [1 ]
机构：
[1] Faculty of Material and Photoelectronic Physics, Xiangtan University
[2] Energy Engineering College, Xiangtan University
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2011年 / 37卷 / 10期
关键词：
Correlation; Image denoising; Infrared image; Thresholding function; Wavelet transform;
D O I：
10.3724/SP.J.1004.2011.01167
中图分类号：
学科分类号：
摘要：
In order to remove noise in infrared images more effectively, an infrared image denoising method based on improved threshold and inter-scale correlations of wavelet transform is proposed. On the one hand, by using the threshold correction scheme and the new thresholding function, the wavelet threshold denoising method is improved. On the other hand, by utilizing inter-scale correlations to estimate wavelet coefficients near a threshold, the accuracy to estimate wavelet coefficients with threshold is increased. Experimental results show that compared to the wavelet threshold denoising method, the proposed method is more effective in infrared image denoising, achieves higher peak signal-to-noise ratio (PSNR), edge preserved index (EPI) and better visual quality, and has a good practicability. Copyright © 2011 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1167 / 1174
页数：7
相关论文
共 17 条
  • [1] Feng Z., Ma Q.-S., Research on infrared image nonlinear enhancement algorithm based on wavelet analysis, Laser and Infrared, 40, 3, pp. 315-318, (2010)
  • [2] Gao S.-B., Cheng Y.-M., Zhao Y.-Q., Pan Q., Wei K., Infrared image denoising based on stationary wavelet transform using tensor, Acta Optica Sinica, 29, 7, pp. 1818-1823, (2009)
  • [3] Li Z.-S., Li W.-L., Yao J.-G., Yang Y.-J., On-site detection of pollution level of insulators based on infrared-thermal-image processing, Proceedings of the CSEE, 30, 4, pp. 132-138, (2010)
  • [4] Weng X.-G., Wang H.-N., Li H., Tao L., Li W.-T., Noise reduction algorithm for MRI images based on dyadic wavelet transform, Journal of Nanjing University of Aeronautics and Astronautics, 41, 6, pp. 753-756, (2009)
  • [5] Donoho D.L., De-noising by soft-thresholding, IEEE Transactions on Information Theory, 41, 3, pp. 613-627, (1995)
  • [6] Donoho D.L., Johnstone I.M., Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistic Association, 90, 432, pp. 1200-1224, (1995)
  • [7] Donoho D.L., Johnstone I.M., Kerkyacharian G., Picard D., Wavelet shrinkage: Asymptopia?, Journal of Royal Statistical Society Series B, 57, 2, pp. 301-369, (1995)
  • [8] Donoho D.L., Johnstone I.M., Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81, 3, pp. 425-455, (1994)
  • [9] Ren H.-R., Zhang P., Wang J.-L., Novel wavelet image denoising method, Infrared and Laser Engineering, 32, 6, pp. 643-646, (2003)
  • [10] Yu M., Yi W.-J., Jiang G.-Y., Image denoise based on inter-scale correlations of Contourlet transform, Opto-Electronic Engineering, 33, 6, (2006)
12