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Proceedings Paper

A DSP-based neural network non-uniformity correction algorithm for IRFPA
Author(s): Chong-liang Liu; Wei-qi Jin; Yang Cao; Xiu Liu
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Paper Abstract

An effective neural network non-uniformity correction (NUC) algorithm based on DSP is proposed in this paper. The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise(FPN).We introduced and analyzed the artificial neural network scene-based non-uniformity correction (SBNUC) algorithm. A design of DSP-based NUC development platform for IRFPA is described. The DSP hardware platform designed is of low power consumption, with 32-bit fixed point DSP TMS320DM643 as the kernel processor. The dependability and expansibility of the software have been improved by DSP/BIOS real-time operating system and Reference Framework 5. In order to realize real-time performance, the calibration parameters update is set at a lower task priority then video input and output in DSP/BIOS. In this way, calibration parameters updating will not affect video streams. The work flow of the system and the strategy of real-time realization are introduced. Experiments on real infrared imaging sequences demonstrate that this algorithm requires only a few frames to obtain high quality corrections. It is computationally efficient and suitable for all kinds of non-uniformity.

Paper Details

Date Published: 5 August 2009
PDF: 7 pages
Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73832O (5 August 2009); doi: 10.1117/12.834894
Show Author Affiliations
Chong-liang Liu, Beijing Institute of Technology (China)
Wei-qi Jin, Beijing Institute of Technology (China)
Yang Cao, Beijing Institute of Technology (China)
Xiu Liu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7383:
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications
Jeffery Puschell; Hai-mei Gong; Yi Cai; Jin Lu; Jin-dong Fei, Editor(s)

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