基于神经网络的红外焦平面光学非均匀性校正改进算法
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  • 英文篇名:An Improved Algorithm for IRFPA Optical Nonuniformity Correction Based on Neural Networks
  • 作者:李谦 ; 杨波 ; 粟宇路 ; 樊佩琦 ; 刘传明 ; 苏俊波
  • 英文作者:LI Qian;YANG Bo;SU Yulu;FAN Peiqi;LIU Chuanming;SU Junbo;Kunming Institute of Physics;
  • 关键词:非均匀性校正 ; 光学非均匀性 ; 直方图均衡化
  • 英文关键词:non-uniformity correction;;optical non-uniformity;;histogram equalization
  • 中文刊名:HWJS
  • 英文刊名:Infrared Technology
  • 机构:昆明物理研究所;
  • 出版日期:2019-03-20
  • 出版单位:红外技术
  • 年:2019
  • 期:v.41;No.315
  • 语种:中文;
  • 页:HWJS201903010
  • 页数:5
  • CN:03
  • ISSN:53-1053/TN
  • 分类号:53-57
摘要
基于场景的非均匀校正依然是红外领域的一个研究热门。神经网络算法是一种较为典型的场景校正算法。本文主要针对神经网络算法本身不能校正光学引入的非均匀性问题,提出了新的改进算法,通过对神经网络输入层的预处理,消除图像的低频噪声,此外,为了消除预处理对图像对比度的影响,本文增加了神经网络的层数,使用双层神经网络对算法进行更新,从而消除了图像对比度下降的现象。实验结果表明,改进的神经网络算法能够有效的改善图像质量,消除图像中光学引入的非均匀性。
        Scene-based non-uniformity correction is still a hot topic in the infrared field. A neural network algorithm is a classical scene-based non-uniformity correction algorithm. This article mainly introduced problems where the classical algorithm cannot correct an optical non-uniformity. We propose an improved algorithm based on the preprocessing layer to correct to the low-frequency noise. In order to eliminate the influence of the image contrast, we add a learning layer that can eliminate the image contrast drop phenomenon. The results of the experiment show that the new algorithm can effectively improve the image quality and eliminate non-uniformity introduced by the optics.
引文
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