Foreground and background separation of sparse infrared image based on combining sensing matrix

Guanghui Ding; Jun Ma; Lei Xin; Feng Li

doi:10.1117/12.2619603

27 March 2022 Foreground and background separation of sparse infrared image based on combining sensing matrix

Guanghui Ding, Jun Ma, Lei Xin, Feng Li

Proceedings Volume 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications; 121690E (2022) https://doi.org/10.1117/12.2619603
Event: Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 2021, Kunming, China

Abstract

A method based on compressed sampling and matrix decomposition to separate foreground and background of infrared sparse images was proposed in this article. A new combinational sensing matrix was used to obtain measurements of original infrared images. The thumbnail and the compressed sampled values are obtained by the combinational sensing matrix at the same time. Rank estimation based on image information entropy and sparse recovery was used to reconstruct foreground and background in the compressed domain. Experiments was carried out on real infrared images. Compared with MaxMedian, TopHat and CLSDM, the signal-to-noise ratio gain and background suppression factor have been significantly improved by using our method.

Citation Download Citation

Guanghui Ding, Jun Ma, Lei Xin, and Feng Li "Foreground and background separation of sparse infrared image based on combining sensing matrix", Proc. SPIE 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 121690E (27 March 2022); https://doi.org/10.1117/12.2619603

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