Image denoising through bivariate shrinkage function in framelet domain
This addresses the problem of image denoising for applications like photography or medical imaging, but it is incremental as it builds on prior wavelet-based denoising techniques.
The paper tackled image denoising by proposing a new method using a bivariate shrinkage function in the framelet domain, which adapts thresholds based on local statistics, resulting in significantly superior image quality and higher PSNR compared to existing methods.
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.