Image denoising through bivariate shrinkage function in framelet domain

TL;DR

This paper introduces a novel image denoising method utilizing a bivariate shrinkage function in the framelet domain, which adaptively estimates coefficients based on local statistics, resulting in improved image quality.

Contribution

The paper presents a new denoising approach combining bivariate shrinkage with framelet domain analysis, enhancing noise reduction effectiveness over existing methods.

Findings

Significantly higher PSNR compared to traditional methods

Superior image quality demonstrated through experiments

Adaptive thresholds improve denoising performance

Abstract

Denoising of coefficients in a sparse domain (e.g. wavelet) has been researched extensively because of its simplicity and effectiveness. Literature mainly has focused on designing the best global threshold. However, this paper proposes a new denoising method using bivariate shrinkage function in framelet domain. In the proposed method, maximum aposteriori probability is used for estimate of the denoised coefficient and non-Gaussian bivariate function is applied to model the statistics of framelet coefficients. For every framelet coefficient, there is a corresponding threshold depending on the local statistics of framelet coefficients. Experimental results show that using bivariate shrinkage function in framelet domain yields significantly superior image quality and higher PSNR than some well-known denoising methods.