Shift-invariant wavelet denoising using interscale dependency
Pei Chen, David Suter · 2005
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/spl sim/0.9 dB is obtained. The proposed approach, siPAB, outperforms siHMT, which is a competitive SI wavelet denoising approach, by 0.1/spl sim/0.5 dB.