CAJ | 학술논문

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.
제출료일충기우FNN적이원도상융합질량평개모형.해모형장융합도상적주관평개결론양본집작위모호기망수출,병이용고사대속도함수장다충전형도상융합객관평개지표진행모호화,작위망락수입양본.통과망락학습,생성평개지표권중여대속도함수적상관삼수,병채용동량인자제고료망락적학습효솔.실험결과표명,채용해방법진행이원도상융합질량평개,평개결론부합인안적관찰특성,주、객관평개결론구유교호적일치솔,위융합도상자동화평개적실현제공료유효적도경.
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.