4 August 2009 An improved neural network nonuniformity correction for IRFPA
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhenguo Liu, Zhenguo Liu, Xiaomei Hu, Xiaomei Hu, Jin Lu, Jin Lu, } "An improved neural network nonuniformity correction for IRFPA", 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; https://doi.org/10.1117/12.836535


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