摘要
提出了一种基于FNN的异源图像融合质量评价模型。该模型将融合图像的主观评价结论样本集作为模糊期望输出,并利用高斯隶属度函数将多种典型图像融合客观评价指标进行模糊化,作为网络输入样本。通过网络学习,生成评价指标权重与隶属度函数的相关参数,并采用动量因子提高了网络的学习效率。实验结果表明,采用该方法进行异源图像融合质量评价,评价结论符合人眼的观察特性,主、客观评价结论具有较好的一致率,为融合图像自动化评价的实现提供了有效的途径。
An evaluation model for different-source image fusion quality based on FNN is proposed. Subjective evaluation conclusion sample sets of fusion images are considered as output of fuzzy expectation. Several classical objective evaluation indexes are fuzzed by Gaussian membership function as network input samples. Related parameters of evaluation index weight and membership function are generated by network learning. Momentum factor is adopted to improve network learning efficiency. Experimental results show that the evaluation results are reasonable to human eyes. The uniformity ratio of subjective and objective evaluation can reach a high rate ,which provides a valuable method for the realization of automatic fusion image evaluation.
出处
《激光与红外》
CAS
CSCD
北大核心
2010年第1期99-102,共4页
Laser & Infrared
基金
国家自然科学基金项目(No.60874106)资助
关键词
图像融合
质量评价
FNN
隶属度函数
异源
image fusion
quality evaluation
FNN
membership function
different-source
