Papers Published

1. Yang, J; Yuan, X; Liao, X; Llull, P; Brady, DJ; Sapiro, G; Carin, L, Video compressive sensing using Gaussian mixture models., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 23 no. 11 (November, 2014), pp. 4863-4878 [doi] .
   (last updated on 2023/06/01)

   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.