Generalized-KFCS: Motion estimation enhanced Kalman filtered compressive sensing for video
Type
Conference Object
Change log
Authors
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.
Description
Keywords
Compressed sensing, Image reconstruction, Indexes, Kalman filters, Motion estimation, Sensors, Video sequences
Journal Title
2014 IEEE International Conference on Image Processing, ICIP 2014
Conference Name
2014 IEEE International Conference on Image Processing (ICIP)
Journal ISSN
1522-4880
Volume Title
Publisher
IEEE
Publisher DOI
Sponsorship
Engineering and Physical Sciences Research Council (EP/K033700/1)
This work was partially supported by EPSRC Research Grant EP/K033700/1, the Fundamental Research Funds for the Central Universities (No. 2014JBM149), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars (of State Education Ministry).