Shift-invariant wavelet denoising using interscale dependency

Abstract

Using statistical modeling in the wavelet domain, we address the problem of image denoising. Despite being effective, the denoised images can suffer from the Gibbs-like artifacts, like ringing around the edges and speckles in the smooth regions. We employ shift-invariant (SI) wavelet denoising in order to reduce these unpleasant artifacts. Not only is the visual quality greatly improved but also a PSNR gain of about 0.7∼0.9 dB is obtained. The proposed approach, siPAB, outperforms siHMT, which is a competitive SI wavelet denoising approach, by 0.1∼0.5 dB.