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Remote sensing image denoising based on improved semi-soft threshold

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Abstract

Remote sensing image denoising has important applications in aerospace, geophysical exploration and communication engineering. Traditional wavelet transform cannot represent the details and contours of the image texture effectively, because pseudo-Gibbs effect, ringing and blurring of edge details will be produced during de-noising. In this paper, in view of the shortcomings of the existing directional filter design, an eight-direction filter bank that directly decomposes each direction of the image using each directional subband filter is proposed, which is combined with Laplace pyramid transformation to form an optimized contour transformation. A denoising threshold processing algorithm for remote sensing images based on optimized contourlet transformation is proposed. Compared with the traditional denoising method, the improved algorithm has the characteristics of multi-scale and multi-direction, and can better capture the detailed information of the image. The improved semi-soft threshold function is used to process the transformed coefficients, which can better restore the edge information in the image. The results of a series of simulation experiments show that compared with the traditional wavelet threshold function, the contourlet hard threshold function and contourlet soft threshold function, this method achieves better visual effects and higher PSNR (peak signal to noise ratio) while the image is denoised, and the denoising effect is improved by 0.11%. The proposed new threshold function is feasible and can handle the texture information and edge information of remote sensing image well.

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References

  1. Deng, L.J., Feng, M., Tai, X.C.: The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior. Inf. Fusion 52, 76–89 (2019)

    Article  Google Scholar 

  2. Chen, G., Weng, Q., Hay, G.J., et al.: Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GISci. Remote Sens. 55, 159–182 (2018)

    Article  Google Scholar 

  3. Wang, Y., Qi, Q., Liu, Y., et al.: Unsupervised segmentation parameter selection using the local spatial statistics for remote sensing image segmentation. Int. J. Appl. Earth Obs. Geoinf. 81, 98–109 (2019)

    Article  Google Scholar 

  4. Chen, Y., Huang, T.Z., Deng, L.J., et al.: Group sparsity based regularization model for remote sensing image stripe noise removal. Neurocomputing 267, 95–106 (2017)

    Article  Google Scholar 

  5. Xie, W., Li, Y., Hu, J., et al.: Trainable spectral difference learning with spatial starting for hyperspectral image denoising. Neural Netw. 108, 272–286 (2018)

    Article  Google Scholar 

  6. Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14, 2091–2106 (2005)

    Article  Google Scholar 

  7. Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., et al.: Density estimation by wavelet thresholding. Ann. Stat. 24, 508–539 (1996)

    Article  MathSciNet  Google Scholar 

  8. Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41, 613–627 (1995)

    Article  MathSciNet  Google Scholar 

  9. Donoho, D.L., Johnstone, J.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)

    Article  MathSciNet  Google Scholar 

  10. Li, L., Wang, L., Jia, Z., et al.: A practical medical image enhancement algorithm based on nonsubsampled contourlet transform. J. Med. Imaging Health Inf. 9, 1046–1056 (2019)

    Article  Google Scholar 

  11. Yan, Z., Chen, G., Xu, W., et al.: Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm. Appl. Opt. 57, 9714–9721 (2018)

    Article  Google Scholar 

  12. Yan, Z., Xu, W., Ai, C., et al.: Parametric study on pump noise processing method of continuous wave mud pulse signal based on dual-sensor. J. Pet. Sci. Eng. 178, 987–998 (2019)

    Article  Google Scholar 

  13. Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31, 532–540 (1983)

    Article  Google Scholar 

  14. Paris, S., Hasinoff, S.W., Kautz, J.: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph. 30, 68 (2011)

    Article  Google Scholar 

  15. Rabbani, H.: Image denoising in steerable pyramid domain based on a local Laplace prior. Pattern Recogn. 42, 2181–2193 (2009)

    Article  Google Scholar 

  16. Bhandari, A.K., Kumar, A., Singh, G.K., et al.: Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold. J. Exp. Theor. Artif. Intell. 28, 71–95 (2016)

    Article  Google Scholar 

  17. Ourabia, S., Smara, Y.: A new pansharpening approach based on nonsubsampled contourlet transform using enhanced PCA applied to SPOT and ALSAT-2A satellite images. J. Indian Soc. Remote Sens. 44, 665–674 (2016)

    Article  Google Scholar 

  18. Fang, L., Wang, X., Sun, Y., et al.: Remote sensing image segmentation using active contours based on intercorrelation of nonsubsampled contourlet coefficients. J. Electron. Imaging 25, 061405 (2016)

    Article  Google Scholar 

  19. Li, L., Si, Y., Jia, Z.: A novel brain image enhancement method based on nonsubsampled contourlet transform. Int. J. Imaging Syst. Technol. 28, 124–131 (2018)

    Article  Google Scholar 

  20. Liu, J.X., Wen, X., Yuan, L.M., et al.: A robust approach of watermarking in contourlet domain based on probabilistic neural network. Multimed. Tools Appl. 76, 24009–24026 (2017)

    Article  Google Scholar 

  21. Chen, G.Y., Bui, T.D., Krzyzak, A.: Image denoising using neighbouring wavelet coefficients. Integr. Comput. Aided Eng. 12, 99–107 (2005)

    Article  Google Scholar 

  22. Sadreazami, H., Ahmad, M.O., Swamy, M.N.S.: A study on image denoising in contourlet domain using the alpha-stable family of distributions. Signal Process. 128, 459–473 (2016)

    Article  Google Scholar 

  23. Song, H., Yu, S., Yang, X., et al.: Contourlet-based image adaptive watermarking. Signal Process. Image Commun. 23, 162–178 (2008)

    Article  Google Scholar 

  24. Adelson, E.H., Anderson, C.H., Bergen, J.R., et al.: Pyramid methods in image processing. RCA Eng. 29, 33–41 (1984)

    Google Scholar 

  25. Olkkonen, H., Pesola, P.: Gaussian pyramid wavelet transform for multiresolution analysis of images. Graph. Models Image Process. 58, 394–398 (1996)

    Article  Google Scholar 

  26. Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: European conference on computer vision, pp. 519–534. Springer, Cham (2016)

  27. Mersereau, R., Speake, T.: The processing of periodically sampled multidimensional signals. IEEE Trans. Acoust. Speech Signal Process. 31, 188–194 (1983)

    Article  Google Scholar 

  28. Viscito, E., Allebach, J.P.: The analysis and design of multidimensional FIR perfect reconstruction filter banks for arbitrary sampling lattices. IEEE Trans. Circuits Syst. 38, 29–41 (1991)

    Article  Google Scholar 

  29. Simoncelli, E.P., Adelson, E.H.: Non-separable extensions of quadrature mirror filters to multiple dimensions. Proc. IEEE 78, 652–664 (1990)

    Article  Google Scholar 

Download references

Acknowledgements

This research was financially supported by the Ministry of Science and Technology State Key Support Program (2016YFE0105100), Micro-Nano and Ultra-Precision Key Laboratory of Jilin Province (20140622008JC) and Science and Technology Development Projects of Jilin Province (20180201052GX, 20190201303JC).

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Authors and Affiliations

  1. Key Laboratory of Micro/Nano and Ultra-Precision Manufacturing (Jilin Province), School of Mechatraonic Engineering, Changchun University of Technology, Changchun, 130012, People’s Republic of China

    Sai Lei, Mingming Lu, Jieqiong Lin & Xuemei Yang

  2. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, People’s Republic of China

    Xiaoqin Zhou

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  1. Sai Lei
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  2. Mingming Lu
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  3. Jieqiong Lin
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  4. Xiaoqin Zhou
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  5. Xuemei Yang
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Correspondence to Mingming Lu or Jieqiong Lin.

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Lei, S., Lu, M., Lin, J. et al. Remote sensing image denoising based on improved semi-soft threshold. SIViP 15, 73–81 (2021). https://doi.org/10.1007/s11760-020-01722-3

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  • DOI: https://doi.org/10.1007/s11760-020-01722-3

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