Fast estimation of parameter in wavelet-domain HMT model and its application in image denoising
Zhiyun Xiao · Jisuanji gongcheng · 2004
Although HMT model captures intrascale and interscale dependencies of wavelet coefficients, model parameter training is complex and computationally expensive. To solve this problem, a fast parameter estimation algorithm in which training is not needed was proposed. Firstly, each subband coefficients were classified by spatially adaptive threshold. Secondly, the local statistical features of different classes were computed respectively, and HMT model parameters can be estimated by computing local statistical features. Finally, a non-training HMT was applied to image denoising. Experimental results show that this fast parameter estimation algorithm can not only reduce computing expense and accelerate computation, but also provide an improved denoising performance of PSNR and human vision beyond other methods.