Adaptive filter and threshold for image denoising in new generation wavelet

Authors

Keywords:

image denoising; median filter; bayesian thresholding; curvelet transform

Abstract

In reality, the nature images have the noise values because of many reasons. These values make the quality of images to decrease. Wavelet transform is proposed for denoising and it gives the better results. But with curvelet transform, one of the new generations of wavelet, the quality of images continues to be improved. In this paper, my proposed method is to combine filter and threshold to calculate the denoising coefficients in curvelet domain. The result of proposed method is compared with other previous methods and shows an improvement.

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References

G. Strang (1989). Wavelets and dilation equations: A brief introduction. SIAM Review, 31, 4.

Tim Edwards (1992). Discrete Wavelet Transforms: Theory and Implementation.

Marcin Kociolek, Andrzej Materka, Michal Strzelecki and Piotr Szczypínski (2001). Discrete Wavelet transform – derived features for digital image texture analysis. Proc. Of International Conference on Signals and Electronic Systems, 163-168.

N.T.Binh, Ashish Khare (2013). Image Denoising, Deblurring and Object Tracking, A new Generation wavelet based approach. LAP LAMBERT Academic Publishing.

Minh N. Do and Martin Vetterli (2005). The contourlet transform: an efficient directional multiresolution image representation. IEEE, IEEE Trans, Img. Processing, 2091-2106.

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Received: 04-06-2020
Accepted: 04-06-2020
Published: 07-12-2016

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How to Cite

Tuyet, V. T. H. (2016). Adaptive filter and threshold for image denoising in new generation wavelet. HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, 6(2), 89–96. Retrieved from https://journalofscience.ou.edu.vn/index.php/tech-en/article/view/394

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Copyright (c) 2016 Vo Thi Hong Tuyet

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