Symmetric Normal Inverse Gaussian and Structural Similarity Based Image Denoising

2012 ◽  
pp. 103-111 ◽  
Author(s):  
Yuanjiang Li ◽  
Yuehua Li
Keyword(s):  
Image Denoising ◽  
Structural Similarity ◽  
Inverse Gaussian ◽  
Normal Inverse Gaussian

Multivariate Shrinkage for Image Denoising in Shearlet Domain

2012 ◽  
Vol 239-240 ◽  
pp. 966-969
Author(s):  
Cheng Zhi Deng
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Threshold Function ◽  
Maximum A Posteriori ◽  
Inverse Gaussian ◽  
A Posteriori ◽  
Multivariate Normal ◽  
Gaussian Probability Density Function ◽  
Normal Inverse Gaussian

A new multivariate threshold function for image denoising in the shearlet transfrom is proposed. The new threshod exploits a multivariate normal inverse gaussian probability density function to model neighboring shearlet coefficients. Under this prior, a multivariate Bayesian shearlet estimator is derived by using the maximum a posteriori rule. Experimental results show that the new method achieves state-of-art performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index and visual quality than existing shearlet-based image denoising methods.

scholarly journals Normal Inverse Gaussian Model-Based Image Denoising in the NSCT Domain

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jian Jia ◽  
Yongxin Zhang ◽  
Li Chen ◽  
Zhihua Zhao
Keyword(s):  
Image Denoising ◽  
Gaussian Model ◽  
Structural Similarity ◽  
Linear Interpolation ◽  
Block Matching ◽  
Bayesian Estimator ◽  
Threshold Function ◽  
Inverse Gaussian ◽  
Model Based ◽  
Normal Inverse Gaussian

The objective of image denoising is to retain useful details while removing as much noise as possible to recover an original image from its noisy version. This paper proposes a novel normal inverse Gaussian (NIG) model-based method that uses a Bayesian estimator to carry out image denoising in the nonsubsampled contourlet transform (NSCT) domain. In the proposed method, the NIG model is first used to describe the distributions of the image transform coefficients of each subband in the NSCT domain. Then, the corresponding threshold function is derived from the model using Bayesian maximuma posterioriprobability estimation theory. Finally, optimal linear interpolation thresholding algorithm (OLI-Shrink) is employed to guarantee a gentler thresholding effect. The results of comparative experiments conducted indicate that the denoising performance of our proposed method in terms of peak signal-to-noise ratio is superior to that of several state-of-the-art methods, including BLS-GSM, K-SVD, BivShrink, and BM3D. Further, the proposed method achieves structural similarity (SSIM) index values that are comparable to those of the block-matching 3D transformation (BM3D) method.

LOCALLY ADAPTIVE MULTISCALE BAYESIAN METHOD FOR IMAGE DENOISING BASED ON BIVARIATE NORMAL INVERSE GAUSSIAN DISTRIBUTIONS

2008 ◽  
Vol 06 (04) ◽  
pp. 653-664 ◽  
Author(s):  
MOHAMAD FOROUZANFAR ◽  
HAMID ABRISHAMI MOGHADDAM ◽  
SONA GHADIMI
Keyword(s):  
Statistical Model ◽  
Image Denoising ◽  
Minimum Mean Square Error ◽  
Model Parameters ◽  
Inverse Gaussian ◽  
Wavelet Coefficients ◽  
Bivariate Normal ◽  
Wide Range ◽  
Heavy Tailed ◽  
Normal Inverse Gaussian

Recently, the use of wavelet transform has led to significant advances in image denoising applications. Among wavelet-based denoising approaches, the Bayesian techniques give more accurate estimates. Considering interscale dependencies, these estimates become closer to the original image. In this context, the choice of an appropriate model for wavelet coefficients is an important issue. The performance can also be improved by estimating model parameters in a local neighborhood. In this paper, we propose the bivariate normal inverse Gaussian (NIG) distribution, which can model a wide range of heavy-tailed to less heavy-tailed processes, to model the local wavelet coefficients at adjacent scales. We will show that this new statistical model is superior to the conventional generalized Gaussian (GG) model. Then, a minimum mean square error-based (MMSE-based) Bayesian estimator is designed to effectively remove noise from wavelet coefficients. Exploiting this new statistical model in the dual-tree complex wavelet domain, we achieved state-of-the-art performance among related recent denoising approaches, both visually and in terms of peak signal-to-noise ratio (PSNR).

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