IEEE Trans Image Process - Multivariate compressive sensing for image reconstruction in the wavelet domain: using scale mixture models.

Tópicos

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Resumo

Most wavelet-based reconstruction methods of compressive sensing (CS) are developed under the independence assumption of the wavelet coefficients. However, the wavelet coefficients of images have significant statistical dependencies. Lots of multivariate prior models for the wavelet coefficients of images have been proposed and successfully applied to the image estimation problems. In this paper, the statistical structures of the wavelet coefficients are considered for CS reconstruction of images that are sparse or compressive in wavelet domain. A multivariate pursuit algorithm (MPA) based on the multivariate models is developed. Several multivariate scale mixture models are used as the prior distributions of MPA. Our method reconstructs the images by means of modeling the statistical dependencies of the wavelet coefficients in a neighborhood. The proposed algorithm based on these scale mixture models provides superior performance compared with many state-of-the-art compressive sensing reconstruction algorithms.

Resumo Limpo

waveletbas reconstruct method compress sens cs develop independ assumpt wavelet coeffici howev wavelet coeffici imag signific statist depend lot multivari prior model wavelet coeffici imag propos success appli imag estim problem paper statist structur wavelet coeffici consid cs reconstruct imag spars compress wavelet domain multivari pursuit algorithm mpa base multivari model develop sever multivari scale mixtur model use prior distribut mpa method reconstruct imag mean model statist depend wavelet coeffici neighborhood propos algorithm base scale mixtur model provid superior perform compar mani stateoftheart compress sens reconstruct algorithm

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