Journal of Systems Engineering and Electronics ›› 2012, Vol. 23 ›› Issue (4): 618-624.doi: 10.1109/JSEE.2012.00077


Adaptive nonuniformity correction for IRFPA sensors based on neural network framework

Junqi Bai1, Hongyi Hou2, Chunguang Zhao1, Ning Sun1, and Xianya Wang3,*   

  1. 1. The 28th Institute of China Electronics Technology Group Corporation, Nanjing 210007, P. R. China;
    2. Department of Mathematics, Nanjing University, Nanjing 210093, P. R. China;
    3. School of Electronic Engineering and Optoelectronic Technique, Nanjing University of Science and Technology,
        Nanjing 210094, P. R. China
  • Online:2012-08-21 Published:2010-01-03


For infrared focal plane array sensors, imagery is degraded during signal acquisition, particularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimates detector-level and readoutchannel-level correction parameters using neural network approaches. Firstly, an improved neural network framework is designed to compute the desired output. Secondly, an adaptive learning rate rule is used in the gain and offset parameter estimation process. Experimental results show the proposed algorithm can achieve a faster convergence speed and better stability, remove nonuniformity and track parameters drift effectively, and present a good adaptability to scene changes and nonuniformity conditions.