Journal of Mathematical Imaging and Vision

, Volume 49, Issue 2, pp 434–453 | Cite as

An Efficient Remote Sensing Image Denoising Method in Extended Discrete Shearlet Domain

  • Xiang-Yang Wang
  • Yang-Cheng Liu
  • Hong-Ying Yang


Denoising of images is one of the most basic tasks of image processing. It is a challenging work to design a edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method, it not only can exactly compute the shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. Based on extended DST, an image denoising using fuzzy support vector machine (FSVM) is proposed. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the extended DST. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in extended DST domain, and the FSVM model is obtained by training. Then the extended DST detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by FSVM training model. Finally, the detail subbands of extended DST coefficients are denoised by using the adaptive Bayesian threshold. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.


Image denoising Extended discrete Shearlet transform Fuzzy support vector machine Adaptive Bayesian threshold 



This work was supported by the National Natural Science Foundation of China under Grant Nos. 61272416, 60873222, & 60773031, the Open Project Program of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) under Grant No. 30920130122006, the Open Foundation of Zhejiang Key Laboratory for Signal Processing under Grant No. ZJKL_4_SP-OP2013-01, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. L2013407.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Xiang-Yang Wang
    • 1
    • 2
  • Yang-Cheng Liu
    • 1
  • Hong-Ying Yang
    • 1
  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianP.R. China
  2. 2.Jiangsu Key Laboratory of Image and Video Understanding for Social SafetyNanjing University of Science and TechnologyNanjingP.R. China

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