Multivariate Shrinkage for Image Denoising in Shearlet Domain
邓承志 DENG Cheng-zhi · Applied Mechanics and Materials · 2012
A new multivariate threshold function for image denoising in the shearlet transfrom is proposed. The new threshod exploits a multivariate normal inverse gaussian probability density function to model neighboring shearlet coefficients. Under this prior, a multivariate Bayesian shearlet estimator is derived by using the maximum a posteriori rule. Experimental results show that the new method achieves state-of-art performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index and visual quality than existing shearlet-based image denoising methods.