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Sensors 2016, 16(3), 318; doi:10.3390/s16030318

Compressive Video Recovery Using Block Match Multi-Frame Motion Estimation Based on Single Pixel Cameras

1
School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, China
2
China State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3
Mechanical and Biomedical Engineering Department, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 30 December 2015 / Revised: 18 February 2016 / Accepted: 26 February 2016 / Published: 2 March 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3983 KB, uploaded 2 March 2016]   |  

Abstract

Compressive sensing (CS) theory has opened up new paths for the development of signal processing applications. Based on this theory, a novel single pixel camera architecture has been introduced to overcome the current limitations and challenges of traditional focal plane arrays. However, video quality based on this method is limited by existing acquisition and recovery methods, and the method also suffers from being time-consuming. In this paper, a multi-frame motion estimation algorithm is proposed in CS video to enhance the video quality. The proposed algorithm uses multiple frames to implement motion estimation. Experimental results show that using multi-frame motion estimation can improve the quality of recovered videos. To further reduce the motion estimation time, a block match algorithm is used to process motion estimation. Experiments demonstrate that using the block match algorithm can reduce motion estimation time by 30%. View Full-Text
Keywords: single pixel camera; motion estimation; video sampling; compressive sensing single pixel camera; motion estimation; video sampling; compressive sensing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bi, S.; Zeng, X.; Tang, X.; Qin, S.; Lai, K.W.C. Compressive Video Recovery Using Block Match Multi-Frame Motion Estimation Based on Single Pixel Cameras. Sensors 2016, 16, 318.

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