基于CNN-WF的高灵敏紫外成像仪中的图像配准与融合  

Image Registration and Fusion of High Sensitive Ultraviolet Imager Based on CNN-WF

作　　者：侯思祖[1] 刘雅婷 陈天威 HOU Sizu;LIU Yating;CHEN Tianwei(School of Electrical and Electronic Engin.,North China Electric Power University,Baoding 071003,CHN)

机构地区：[1]华北电力大学电气与电子工程学院,河北保定071003

出　　处：《半导体光电》2021年第4期596-602,共7页Semiconductor Optoelectronics

基　　金：国家重点研发计划项目(2018YFF01011900)。

摘　　要：针对现有紫外成像仪中紫外光与可见光图像配准实时性差,精度不高等问题,提出一种基于卷积神经网络(Convolutional Neural Networks,CNN)与小波融合(Wavelet Fusion,WF)的紫外光与可见光图像配准融合方法,并将其应用于高灵敏紫外成像仪中。首先,结合刚体变换和卷积神经网络对采集到的图像数据进行参数模型预训练,通过自主挖掘图像特征寻找到最优空间变换参数,实现紫外光图像与可见光图像的精确配准;其次,利用二维小波分解与重构算法实现紫外光与可见光图像的融合。实验结果表明,所提方法的紫外光图像与可见光图像配准速度快,叠加精度高,且具有良好的稳定性。Aiming at the problems of poor real-time performance and low accuracy of the existing UV imagers in UV and visible image registration,a method of UV and visible image registration fusion based on convolutional neural network(CNN)and wavelet fusion(WF)is proposed and applied to high sensitive UV imager.Firstly,the parameter model of the collected image data is pre-trained by combining the rigid body transformation and convolution neural network,and the optimal spatial transformation parameters are found by self-mining image features to achieve accurate registration of UV image and visible image.Secondly,twodimensional wavelet decomposition and reconstruction algorithm is used to realize the fusion of UV and visible images.Experimental results show that the proposed method has fast registration speed,high overlay accuracy and good stability.

关 键 词：卷积神经网络 紫外成像仪 图像配准 小波融合 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]