Locally Adaptive Shearlet Denoising Based on Bayesian MAP Estimate
Zhiping Dan, Xi Chen, Haitao Gan, Changxin Gao · 2011
A locally adaptive Bayesian estimate for image denoising is proposed by exploiting the correlation among image shear let coefficients in a sub-band. The Laplacian distribution can model a wide range of process, from heavy-tailed to less heavy-tailed processes. This paper deduces Laplacian prior distribution based the MAP estimate formula and sub-band adaptive threshold. Finally, a simulation is carried out to show the effectiveness of the new estimate. Experiment results demonstrate that compared with classical sub-band adaptive algorithms, the new denoising method has significantly increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect.