Convolutional Gaussian Mixture Models with Application to Compressive Sensing

Published

Conference Paper

© 2018 IEEE. Gaussian mixture models (GMM) have been used to statistically represent patches in an image. Extending from small patches to an entire image, we propose a convolutional Gaussian mixture models (convGMM) to model the statistics of an entire image and apply it for compressive sensing (CS). We present the algorithm details for learning a convGMM from training images by maximizing the marginal log-likelihood estimation (MMLE). The learned convGMM is used to perform model-based compressive sensing, using the convGMM as a model of the underlying image. In addition, a key feature of our method is that all of the training and reconstruction process could be fast and efficient calculated in the frequency-domain by 2-dimensional fast Fourier transforms (2d-FFTs). The performance of the convGMM on CS is demonstrated on several image sets.

Full Text

Duke Authors

Cited Authors

  • Wang, R; Liao, X; Guo, J

Published Date

  • August 29, 2018

Published In

  • 2018 Ieee Statistical Signal Processing Workshop, Ssp 2018

Start / End Page

  • 578 - 582

International Standard Book Number 13 (ISBN-13)

  • 9781538615706

Digital Object Identifier (DOI)

  • 10.1109/SSP.2018.8450817

Citation Source

  • Scopus