Lapped transform-based image denoising with the generalised Gaussian prior. (1st January 2014)

• Record Type: Journal Article
• Authors: Nath, Vijay Kumar; Hazarika, Deepika; Mahanta, Anil
• Is Part Of: International journal of computational vision and robotics. Volume 4:Number 1/2(2014)
• Page Start: 55
• Page End: 74
• Publication Date: 2014-01-01
• Subjects: lapped transform -- generalised Gaussian distribution -- image denoising -- Kolomogrov-Smirnov test -- KS -- Bayesian MMSE estimator -- statistical modelling; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

We introduce a new image denoising method based on the statistical modelling of dyadic rearranged lapped transform (LT) coefficients. Based on Kolomogrov-Smirnov (KS) goodness of fit test, we have shown that the statistical distribution of the dyadic rearranged LT coefficients in a subband is best approximated by the generalised Gaussian distribution. A Bayesian minimum mean square error (MMSE) estimator is used to obtain the estimate of noise free coefficients, which is based on modelling the global distribution of the dyadic rearranged LT coefficients using generalised Gaussian distribution. The LT-based image denoising method with generalised Gaussian prior shows highly encouraging (both objective and subjective) results when compared to several well-known image denoising methods.