An Anisotropic Diffusion Adaptive Filter for Image Denoising and Restoration Applied on Satellite Remote Sensing Images

A Case Study


  • M. Gatcha AAID Laboratory Faculty of Exact Sciences and Computers, Ziane Achour University, Algeria
  • F. Messelmi DMM Laboratory, Faculty of Exact Sciences and Computers, Ziane Achour University, Algeria
  • S. Saadi AAID Laboratory Faculty of Exact Sciences and Computers, Ziane Achour University, Algeria
Volume: 12 | Issue: 6 | Pages: 9715-9719 | December 2022 |


This paper proposes an operating approach based on the anisotropic diffusion method to restore and denoise Satellite Remote Sensing Images (SRSIs). The contents of the approach are the motion by mean curvature to detect the noise direction for each degraded pixel and preserve the original edges of the image, and the gradient in the Gaussian kernel which restores the degraded pixel locally, assuring the estimation of its original value and saving the contrast of the image. The algorithm, concluded by our proposed system, treats noised SRSIs regardless of noise type, so better restoration is achieved. Experiments of the proposed system and of other approaches were conducted in MATLAB in order to demonstrate the efficiency of the proposed approach and its performance was confirmed through evaluation with PSNR and SSIM.


Image restoration, anisotropic diffusion, regularization, Satellite Remote Sensing Images


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D. Marr and E. Hildreth, "Theory of edge detection," Proceedings of the Royal Society of London, Series B. Biological Sciences, vol. 207, no. 1167.

J. J. Koenderink, "The structure of images," Biological Cybernetics, vol. 50, pp. 363–370, 1984. DOI:

P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, Jul. 1990. DOI:

L. Alvarez, P.-L. Lions, and J.-M. Morel, "Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. II," SIAM Journal on numerical analysis, vol. 29, no. 3, pp. 845–866, Jun. 1992. DOI:

I. Tellala, N. Amardjia, and A. Kesmia, "Α Modified EMD-ACWA Denoising Scheme using a Noise-only Model," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5470–5476, Apr. 2020. DOI:

N. Diffellah, R. Hamdini, and T. Bekkouche, "Removal of Multiplicative Gamma Noise from Images via SRAD Model Amelioration," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7917–7921, Dec. 2021. DOI:

M. V. Sarode and P. R. Deshmukh, "Image Sequence Denoising with Motion Estimation in Color Image Sequences," Engineering, Technology & Applied Science Research, vol. 1, no. 6, pp. 139–143, Dec. 2011. DOI:

J. Gao, Digital Analysis of Remotely Sensed Imagery, 1st ed. McGraw Hill, 2009.

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, "Remote sensing image fusion using the curvelet transform," Information Fusion, vol. 8, no. 2, pp. 143–156, Apr. 2007. DOI:

J. Kang and W. Zhang, "Quickbird Remote Sensing Image Denoising Using Wavelet Packet Transform," in 2008 Second International Symposium on Intelligent Information Technology Application, Shanghai, China, Sep. 2008, vol. 3, pp. 315–318. DOI:

M.-G. Hu, J.-F. Wang, and Y. Ge, "Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis," Sensors (Basel, Switzerland), vol. 9, no. 11, pp. 8669–8683, 2009. DOI:

W. Wang and Y. Li, "Bayesian Denoising for Remote Sensing Image Based on Undecimated Discrete Wavelet Transform," in 2009 International Conference on Information Engineering and Computer Science, Wuhan, China, Sep. 2009. DOI:

B. Moseley, V. Bickel, I. G. López-Francos, and L. Rana, "Extreme Low-Light Environment-Driven Image Denoising over Permanently Shadowed Lunar Regions with a Physical Noise Model," in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, Jun. 2021, pp. 6313–6323. DOI:

H. Sun, M. Liu, K. Zheng, D. Yang, J. Li, and L. Gao, "Hyperspectral Image Denoising via Low-Rank Representation and CNN Denoiser," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 716–728, 2022. DOI:

N. A. Golilarz, H. Gao, S. Pirasteh, M. Yazdi, J. Zhou, and Y. Fu, "Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain," Remote Sensing, vol. 13, no. 1, Jan. 2021, Art. No. 101. DOI:

V. S. Riabenki and S. K. Godounov, Theory of Difference Schemes: An Introduction. Algeria: University Publication Office, 1987.

A. Boucher, Image Processing - Spatial Convolution. Montreal, QC, Canada: University of Montreal, 2016.

V. Choqueuse, Convolution Product - Principle and Propieties. 2016.

Z. Li, Z. Qiao, and T. Tang, Numerical Solution of Differential Equations. Cambridge University Press, 2017. DOI:

J. C. Russ, The Image Processing Cookbook, 4th ed. CreateSpace Independent Publishing Platform, 2017.

"MathWorks - Makers of MATLAB and Simulink - MATLAB & Simulink," Mathworks.


How to Cite

M. Gatcha, F. Messelmi, and S. Saadi, “An Anisotropic Diffusion Adaptive Filter for Image Denoising and Restoration Applied on Satellite Remote Sensing Images: A Case Study”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9715–9719, Dec. 2022.


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