Compressive video recovery using block match multi-frame motion estimation based on single pixel cameras

Sheng Bi; Xiao Zeng; Xin Tang; Shujia Qin; King Wai Chiu Lai

doi:10.3390/s16030318

Author(s)

Sheng Bi
Xiao Zeng
Xin Tang
Shujia Qin
King Wai Chiu Lai

Detail(s)

Original language	English
Article number	318
Journal / Publication	Sensors (Switzerland)
Volume	16
Issue number	3
Online published	2 Mar 2016
Publication status	Published - Mar 2016

Link(s)

DOI	DOI https://doi.org/10.3390/s16030318 Final Published version
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Attachment(s)	Documents 6990266.pdf Final Published version, 3.89 MB, PDF Publisher's Copyright Statement This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-84959462490&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(3e55473f-70ea-4987-be95-6d971c7ca118).html

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%.