An image NSCT-HMT model based on copula entropy multivariate Gaussian scale mixtures

作者：

Highlights：

• By studying the statistical property of NSCT coefficients, we redefine the generalized neighborhood relationship of them.

• The copula entropy is first applied to the correlation measurement of multiscale analysis coefficients.

• A novel copula entropy multivariate Gaussian scale mixtures distribution and the corresponding HMT model are proposed.

• Our method provides a way to further extend the application of copula entropy to other image processing area.

摘要

•By studying the statistical property of NSCT coefficients, we redefine the generalized neighborhood relationship of them.•The copula entropy is first applied to the correlation measurement of multiscale analysis coefficients.•A novel copula entropy multivariate Gaussian scale mixtures distribution and the corresponding HMT model are proposed.•Our method provides a way to further extend the application of copula entropy to other image processing area.

论文关键词：NSCT,Gaussian copula,Copula entropy multivariate Gaussian scale mixtures,HMT,Image denoising

论文评审过程：Received 7 November 2018, Revised 29 July 2019, Accepted 11 December 2019, Available online 14 December 2019, Version of Record 7 March 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.105387