A group matching pursuit for image reconstruction

作者：

Highlights：

• GMP improved the reconstruction result with the aggregation of the wavelet domain.

• With the group correlation value, GMP can accurately locate the position of large coefficients.

• The estimated image edge is used to guide the locations of significant coefficients.

• The group coefficients are modeled by a multivariate Gaussian distribution and solved by MAP estimate.

摘要

•GMP improved the reconstruction result with the aggregation of the wavelet domain.•With the group correlation value, GMP can accurately locate the position of large coefficients.•The estimated image edge is used to guide the locations of significant coefficients.•The group coefficients are modeled by a multivariate Gaussian distribution and solved by MAP estimate.

论文关键词：Compressive sensing,Group matching pursuit,Neighborhood structures,Wavelet transform

论文评审过程：Received 25 December 2015, Revised 26 September 2016, Accepted 17 October 2016, Available online 19 October 2016, Version of Record 26 October 2016.

论文官网地址：https://doi.org/10.1016/j.image.2016.10.002