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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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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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  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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