A new wavelet-based fuzzy single and multi-channel image denoising

Saeedi, Jamal; Moradi, Mohammad Hassan; Faez, Karim

doi:10.1016/j.imavis.2010.04.004

Cited by 42 publications

(35 citation statements)

References 37 publications

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“…It is well known that the more adjacent points are more similar in magnitude. So use a fuzzy function m(l, k) of magnitude similarity and a fuzzy function s(l, k) of spatial similarity, which is defined as (Saeedi et al, 2010) According the two fuzzy functions, can get adaptive weight w(l, k) for each neighboring coefficient Equation 4:…”

Section: Fuzzy Modelmentioning

confidence: 99%

Fuzzy Shrink Image Denoising Using Smoothing Spline Estimation

Rajathi¹,

Rangarajan²,

R³

et al. 2014

Journal of Computer Science

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The image data is normally corrupted by additive noise during acquisition. This reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to restore the original image. In proposed method a wavelet shrinkage algorithm based on fuzzy logic and the DT-DWT scheme is used. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This model differentiates the important coefficients and the coefficients belong to image discontinuity and noisy coefficients. This fuzzy model is used to enhance the wavelet coefficients' information in the shrinkage step which uses the fuzzy membership function to shrink wavelet coefficients based on the fuzzy feature. The effectiveness of image denoising depends upon the estimation of noise variance of noisy image, the noise variance is estimated using smoothing spline Estimation. This study examine image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Simulation result shows our approach achieves a substantial improvement in both PSNR and Visual quality.

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Section: Fuzzy Modelmentioning

confidence: 99%

Fuzzy Shrink Image Denoising Using Smoothing Spline Estimation

Rajathi¹,

Rangarajan²,

R³

et al. 2014

Journal of Computer Science

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“…It was found that the wavelet based method provides superior performance [63]. Saeedi et al [64] used fuzzy logic to incorporate the inter-relation between different channels during the wavelet denoising process and found improved performance compared with the standard single channel wavelet denoising methods [64]. Tian et al [65] proposed a nonparametric model to formulate the marginal distribution of wavelet coefficients in an adaptive manner and integrated it into a Bayesian inference framework to drive a maximum a posterior estimation based image denoising method with improved performance [65].…”

Section: Multivariate Wavelet Denoising Algorithmmentioning

confidence: 99%

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Lai

Xiang

2012

Energies

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In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporates the mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

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“…Recently, wavelet transformation fuzzy set theory [43], and Parzen window estimate [44] technique are applied to create multi-level thresholing methods. More details on image thresholding methods can be found in the article written by Sezgin and Sankur [45].…”

Section: Thresholdingmentioning

confidence: 99%