Image Denoising Based on A New Wavelet Statistical Model

Infrared image denoising using mixed statistical model in nonsubsampled contourlet domain

2015 IEEE International Conference on Information and Automation ◽

10.1109/icinfa.2015.7279327 ◽

2015 ◽

Author(s):

Gang Liu ◽

Xitao Zhang ◽

Cuisheng Wu ◽

Heng Zhou

Keyword(s):

Statistical Model ◽

Image Denoising ◽

Infrared Image

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Image Denoising Using Mixed Statistical Model in Nonsubsampled Contourlet Transform Domain

ACTA PHOTONICA SINICA ◽

10.3788/gzxb20124106.0751 ◽

2012 ◽

Vol 41 (6) ◽

pp. 751-756

Author(s):

殷明 YIN Ming ◽

刘卫 LIU Wei

Keyword(s):

Statistical Model ◽

Image Denoising ◽

Contourlet Transform ◽

Transform Domain ◽

Nonsubsampled Contourlet Transform

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

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691308002562 ◽

2008 ◽

Vol 06 (04) ◽

pp. 653-664 ◽

Cited By ~ 6

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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