红外技术Issue(4)：265-271,7.
基于非局部均值滤波与神经网络的红外焦平面阵列非均匀性校正算法
Neural Network Nonuniformity Correction Algorithm for Infrared Focal Plane Array Based on Non-local Means Filter
摘要
Abstract
Traditional neural network nonuniformity correction method has the drawback of low convergence speed and is easy to generate ghosting artifacts. To overcome these problems, a neural network nonuniformity correction algorithm based on the non-local means filter is proposed for the infrared focal plane array in this study. To estimate the true image with a higher degree of confidence, the non-local means filter is employed to replace the average filter which is used in the traditional neural network method for its strong ability of edge preservation and global optimization. A variable learning rate is designed in the recursive parameter update process to eliminate the ghosting artifacts more effectively. The performance of the proposed method is tested with two infrared image sequences, which are contaminated with high spatial frequency and low spatial frequency nonuniformity, respectively. Compared with other well-established nonuniformity correction methods, our method has the strength in significantly increasing the convergence speed and meanwhile reducing the ghosting artifacts.关键词
非均匀性校正/神经网络/非局部均值滤波/收敛速度/鬼影Key words
nonuniformity correction/neural network/non-local means filter/convergence speed/ghosting artifacts分类
信息技术与安全科学引用本文复制引用
张菲菲,王文龙,马国锐,谢伟,陈王丽,秦前清..基于非局部均值滤波与神经网络的红外焦平面阵列非均匀性校正算法[J].红外技术,2015,(4):265-271,7.基金项目
国家863计划资助项目，编号2013AA122301；国家自然科学基金项目，编号61001187；湖北省自然科学基金面上项目，编号2014CFB461；华中师范大学中央高校基本科研业务费项目，编号CCNU14A05017。 （）
