NDLI: Spatially adaptive image denoising using inter-scale dependence in directionlet domain

Content Provider	IET Digital Library
Author	Sethunadh, R. Thomas, Tessamma
Abstract	The performance of image processing algorithms can be significantly improved by the application of multi-resolution image representation with directional features. Directionlet transform (DT) is one such representation which has gained popularity over the past few years as an anisotropic, perfect reconstruction and critically sampled basis function with directional vanishing moments along any two directions. In this study, the authors propose a spatially adaptive image denoising scheme for Gaussian noise based on DT by considering the dependences of the directionlet coefficients across different scales. The image is first decomposed using DT and the coefficients so obtained are modelled using a bivariate heavy tailed ‘pdf’ with a local variance parameter to account for inter- and intra-scale dependencies of the coefficients. The DT is made adaptive to the local dominant directions in the image by identifying the dominant directions in the spatially segmented image through the computation of a parameter called ‘directional variance’. Bayesian ‘maximum a posteriori’ estimator is then used to compute the noise free coefficients from the bivariate models of the signal and noise. The denoised image is obtained from the transform coefficients, which were modified using the bivariate shrinkage function, using directional information and inverse DT. Experimental results show that the bivariate shrinkage in directionlet domain achieves better performance than that in wavelet domain, in terms of numerical and perceptual quality.
Starting Page	142
Ending Page	152
Page Count	11
ISSN	17519659
Volume Number	9
e-ISSN	17519667
Issue Number	Issue 2, Feb (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/9/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0112
Journal	IET Image Processing
Publisher Date	2014-08-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bivariate Model Bivariate Shrinkage Function Computer Vision And Image Processing Technique Directional Features Directional Vanishing Moments Directional Variance Directionlet Coefficient Directionlet Domain Gaussian Noise Image Denoising Image Processing Algorithm Image Representation Image Resolution Image Segmentation Integral Transforms Interscale Dependence Multiresolution Image Representation Noise Free Coefficient Optical, Image And Video Signal Processing Sampled Basis Function Spatially Adaptive Image Denoising Statistics Transform Transform Coefficient
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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