Title
Generalized-KFCS: Motion estimation enhanced Kalman filtered compressive sensing for video
Abstract
In this paper, we propose a Generalized Kalman Filtered Compressive Sensing (Generalized-KFCS) framework to reconstruct a video sequence, which relaxes the assumption of a slowly changing sparsity pattern in Kalman Filtered Compressive Sensing [1, 2, 3, 4]. In the proposed framework, we employ motion estimation to achieve the estimation of the state transition matrix for the Kalman filter, and then reconstruct the video sequence via the Kalman filter in conjunction with compressive sensing. In addition, we propose a novel method to directly apply motion estimation to compressively sensed samples without reconstructing the video sequence. Simulation results demonstrate the superiority of our algorithm for practical video reconstruction.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7025259
ICIP
Keywords
Field
DocType
generalized-kfcs,kalman filters,video sampling,matrix algebra,video compression,data compression,motion estimation,image reconstruction,image sampling,compressed sensing,state transition matrix estimation,video coding,image sequences,video reconstruction,generalized kalman filtered compressive sensing,video sequence
Iterative reconstruction,Computer vision,Block-matching algorithm,Quarter-pixel motion,Pattern recognition,Fast Kalman filter,Computer science,Motion compensation,Kalman filter,Artificial intelligence,Motion estimation,Video denoising
Conference
ISSN
Citations 
PageRank 
1522-4880
0
0.34
References 
Authors
12
3
Name
Order
Citations
PageRank
Xin Ding171.79
Wei Chen2266.38
Ian J. Wassell328835.10