无参考自适应光学图像质量评价  

No-reference Adaptive Optical Image Quality Assessment

作　　者：邱小霞 鲍华 高国庆[1,2,3,4] 张莹[1,2,3] 何春元[4] 李淑琪 QIU Xiaoxia;BAO Hua;GAO Guoqing;ZHANG Ying;HE Chunyuan;LI Shuqi(Key Laboratory on Adaptive Optics,Chinese Academy of Sciences,Chengdu 610000,China;Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610000,China;University of Chinese Academy of Sciences,Beijing 100000,China;University of Electronic Science and Technology,Chengdu 611000,China)

机构地区：[1]中国科学院自适应光学重点实验室,成都610000 [2]中国科学院光电技术研究所,成都610000 [3]中国科学院大学,北京100000 [4]电子科技大学,成都611000

出　　处：《电光与控制》2023年第3期48-53,共6页Electronics Optics & Control

基　　金：国家自然科学基金(11727805)。

摘　　要：自适应光学(AO)成像系统受残余大气湍流、闭环跟踪误差和光电探测噪声等因素的影响,成像结果参差不齐,不利于后期图像筛选和事后处理,故需要对图像质量进行评价。传统图像质量评价方法对自适应光学图像质量的评价不可靠,甚至会出现评价结果与实际背离的情况。针对上述问题,根据自适应光学系统的成像过程,生成具有图像质量标签的自适应光学退化图像数据集,在此基础上采用以ResNet作为主干的深度神经网络,训练得到了用于评价自适应光学图像质量的神经网络模型,在测试集上的Spearman相关系数(SROCC)最佳为0.994。实验结果表明,该方法综合考虑了自适应光学图像成像过程中的多种退化因素,通过训练深度神经网络得到无参考自适应光学图像质量评价模型,评价精度优于其他传统图像质量评价算法。Adaptive Optics(AO) imaging system is affected by residual atmospheric turbulence,closed-loop tracking error and photoelectric detection noise,and imaging results are blurred to varying degrees,which is not conducive to the later image screening and post-processing,thus,it is necessary to evaluate the image quality. Traditional image quality assessment methods are not reliable for no-reference AO image quality assessment,and even the assessment results deviate from the actual situation. Aming at the above problems,according to the imaging process of AO system,an AO degradation image dataset with image quality labels is generated. On this basis,a neural network model for assessing the AO image quality is trained by using a deep neural network with ResNet as the backbone,and the best Spearman’s Rank Order Correlation Coefficient(SROCC) on the dataset is 0.994. The experimental results show that this method comprehensively considers various degradation factors in the process of AO imaging,a no-reference AO image quality assessment model is obtained by training deep neural network,and the assessment accuracy is better than that of other traditional image quality assessement algorithms.

关 键 词：图像质量评价 自适应光学图像 深度神经网络 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] O439[自动化与计算机技术—控制科学与工程]