A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denoising in steerable pyramid domain based on a local Laplace prior
Pattern Recognition
Wavelet thresholding via MDL for natural images
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
Translation-Invariant Contourlet Transform and Its Application to Image Denoising
IEEE Transactions on Image Processing
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This paper presents a new image denoising algorithm based on the modeling of contourlet coefficients in each subband with a normal inverse Gaussian (NIG) probability density function (PDF). This PDF is able to model the heavy-tailed nature of contourlet coefficients and the local parameters model the intrascale dependency between the coefficients. Within this framework, we describe a novel method for image denoising based on designing maximum a posteriori (MAP). Furthermore, the cycle spinning algorithm is employed to modify the Gibbs phenomenon around edges caused by the lack of translation invariance of the contourlet transform. Experimental results prove that the new method can remove Gaussian white noise effectively, reserve image edges better and enhance the peak signal-to-noise ratio.