Title :
Video Compressive Sensing Using Gaussian Mixture Models
Author :
Yang, Jian ; Yuan, Xibo ; Liao, Xiaofeng ; Llull, Patrick ; Brady, David J. ; Sapiro, Guillermo ; Carin, Lawrence
Author_Institution :
Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
Abstract :
A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.
Keywords :
Algorithm design and analysis; Compressed sensing; Gaussian mixture model; Image coding; Image reconstruction; Compressive sensing; Gaussian mixture model; blind compressive sensing; coded aperture compressive temporal imaging (CACTI); dictionary learning; online learning; union-of-subspace model;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2014.2344294
