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

作　　者：张菲菲[1] 王文龙 马国锐[1] 谢伟[3] 陈王丽[1] 秦前清[1] 

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [2]武汉市测绘院,湖北武汉430022 [3]华中师范大学计算机学院,湖北武汉430079

出　　处：《红外技术》2015年第4期265-271,共7页Infrared Technology

基　　金：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

摘　　要：深入剖析传统神经网络非均匀性校正方法收敛速度慢以及易产生"鬼影"现象的主要原因,在此基础上,提出一种基于非局部均值滤波和神经网络的红外焦平面阵列非均匀性校正算法。为了加快收敛速度并减少"鬼影"现象,在神经网络隐含层,利用具有全局寻优且能保持边缘的非局部均值滤波器代替传统的均值滤波器以估计具有更高置信度的真值影像;同时设计可变学习率来自适应地调整每个探测元的非均匀性校正参数的迭代更新过程,以进一步消除"鬼影"。采用两组分别受高空间频率和低空间频率非均匀性干扰的真实红外序列图像进行实验。实验结果表明:相较于目前已有的方法,本文方法不仅具有较快的收敛速度,而且较大程度上抑制了"鬼影"现象的发生。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 

分 类 号：TN911.73[电子电信—通信与信息系统]