Login Register Cart 0
Generic placeholder image

Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Multi-scales Image Denoising Method Based on Joint Confidence Probability of Coefficients

Author(s): Dandan Tan*, Yiming Zhang and Bingxu Han

Volume 13, Issue 4, 2019

Page: [395 - 402] Pages: 8

DOI: 10.2174/1872212112666180925151744

Price: $65

Abstract

Background: It is a classic problem that we estimate the original coefficient from the known coefficient disturbed with noise.

Methods: This paper proposes an image denoising method which combines the dual-tree complex wavelet with good direction selection and translation invariance. Firstly, we determine the expression of probability density function through estimating the parameters by the variance and the fourth-order moment. Secondly, we propose two assumptions and calculate the joint confidence probability of original coefficient under the situation that the disturbed parental and present coefficients from neighborhood scale are known. Finally, we set the joint confidence probability as shrinkage function of coefficient for implementing the image denoising.

Results: The simulation experiment results show that, compared to these traditional methods, this new method can reserve more detail information.

Conclusion: Compared to the current methods, our novel algorithm can remove the most noise and reserve the detail texture in denoising results, which can make better visualization. In addition, our algorithm also shows advantage in PSNR.

Keywords: Image denoising, dual-tree complex wavelet, confidence probability, parameter estimation, threshold, image.

Graphical Abstract

[1]
D.L. Donoho, "De-noising by soft-thresholding", IEEE Trans. Inf. Theory, vol. 41, pp. 613-627, 1995.
[2]
D.L. Donoho, and I.M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage", Am. Stat. Assoc., vol. 90, pp. 1200-1224, 1995.
[3]
H.Y. Gao, and A. G. Bruce, WaveShrink and semisoft shrinkage. SaSci Reasearch Report, No. 39, 1995.
[4]
J.L. Meng, Q. Pan, and H.C. Zhang, "Denoising by multi-scale product coefficient semi- soft thresholding", J. Elec. Inform. Tech., vol. 27, pp. 1649-1652, 2007.
[5]
S.G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression", IEEE Trans. Image Process., vol. 9, pp. 1532-1546, 2000.
[6]
S.G. Chang, B. Yu, and M. Vetterli, "Spatially adaptive wavelet thresholding with context modeling for image denoising", IEEE Trans. Image Process., vol. 9, pp. 1522-1531, 2000.
[7]
L. Sendur, and I.W. Selesnick, "Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency", IEEE Trans. Signal Process., vol. 50, pp. 2744-2756, 2002.
[8]
A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, "A joint interand intrascale statistical model for wavelet based Bayesian image denoising", IEEE Trans. Image Process., vol. 11, pp. 545-557, 2002.
[9]
Y. Li, X. Yang, and Y. Liu, "Image restoration with dual-prior constraint models based on Split Bregman", Opt. Rev., vol. 20, pp. 491-495, 2013.
[10]
N.G. Kingsbury, "The dual tree complex wavelet transform: a new technique for shift invariance and directional filters In ", Proceedings of 8th IEEE Digital Signal Processing Workshop Bryce Canyon, Utah, USA 1998. pp. 86-89.
[11]
N. Kingsbury, "The dual-tree complex wavelet transform with improved orthogonality and symmetry properties In ", Proceedings of IEEE Conference on Image Processing Vancouver, Canada 2000, pp. 375-378.
[12]
A. Baradarani, and R.Y. Yu, "A Dual-tree complex wavelet with application in image denoising In ", IEEE International Conference on Signal Processing and Communications Dubai, United Arab Emirates 2007, pp. 1203-1206.
[13]
S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation. ", IEEE Computer Society, vol. 11, pp. 674-693, 1989.
[14]
E.P. Simoncelli, and E.H. Adelson, "Noise removal via Bayesian wavelet coring In ", IEEE Int. Conf. Image Processing Lausanne, Switzerland 1996
[15]
M. Zhang, and B.K. Gunturk, "multiresolution bilateral filtering for image denoising", IEEE Trans. Image Process., vol. 17, pp. 2324-2333, 2008.
[16]
C. Zhang, J.N. Chi, Z.H. Zhang, and Z.L. Wang, "Removing noise of color images based on edge detection and bilateral filter", Acta Electronica Sinica, vol. 38, pp. 1776-1784, 2010.
[17]
J. Deming, Image denoising processing system of X-ray inspection instrument. C.N. Patent 103164844, 2013.
[18]
D. Qionghai, and W. Yu, Method and system for denoising image.. CN Patent 101739669, 2010,

Rights & Permissions Print Cite
Article Metrics
9
© 2024 Bentham Science Publishers | Privacy Policy