Share Email Print
cover

Proceedings Paper

An improved neural network nonuniformity correction for IRFPA
Author(s): Zhenguo Liu; Xiaomei Hu; Jin Lu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The non-uniformity correction (NUC) is a key part that affects the image quality of IR imaging systems. In this paper, the NUC technique for staring IR imaging system is studied based on the research of the response characteristic and the noise components of IRFPA. Especially, the improvement of the traditional neural network correction method is also studied. According to the theory of neural network, we analyzed the reasons that cause the defects of traditional neural network correction, which are the difficulty for choosing the length of study-step, poor performance of the offset nonuniformity correction and the side-effect of "ghosting", then found methods to improve them. So the correction ability and the applicability of the improved NUC method are enhanced. To test the effect of the new NUC method, we implemented the arithmetic on a hardware platform using the Digital Signal Processor TS201, and made experiments in real terrestrial scene and offshore scene. The results prove that the improved neural network correction arithmetic is effective.

Paper Details

Date Published: 4 August 2009
PDF: 8 pages
Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 738330 (4 August 2009); doi: 10.1117/12.836535
Show Author Affiliations
Zhenguo Liu, Tianjin Jinhang Institute of Technology Physics (China)
Xiaomei Hu, Tianjin Jinhang Institute of Technology Physics (China)
Jin Lu, Tianjin Jinhang Institute of Technology Physics (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)

© SPIE. Terms of Use
Back to Top