吉林大学学报（理学版）2016，Vol.54Issue(5)：1087-1095,9.DOI:10.13413/j.cnki.jdxblxb.2016.05.28
基于边界约束最优投影梯度NMF的TINST域图像融合方法
Image Fusion Method Based on Bound-Constrained Optimal Projection Gradient for NMF in TINST Domain
摘要
Abstract
Aiming at the problem of multi-modality images fusion,we proposed an image fusion method based on bound-constrained optimal projection gradient for non-negative matrix factorization (NMF)in translation invariance nonseparable shearlet transform (TINST)domain.The problem of low fusion accuracy in some existing typical fusion methods was solved effectively.Images were decomposed to some subbands by translation invariance nonseparable shearlet transform. The low-frequency subband coefficients were regarded as original observed data,and the low-frequency subband coefficients were obtained by bound-constrained optimal projection gradient for NMF algorithm.High-frequency directional suband coefficients were used as external input excitation and edge intensity was served as linking strength of each neuron in pulse coupled neural networks (PCNN) and after the fire processing and compare-selection computing, fused high-frequency directional suband coefficients were obtained.Finally,all the fused subbands were reconstructed to an image by translation invariance nonseparable shearlet inverse transform.In order to verify the efficiency of the proposed method,some compared fusion experiments were implemented on several sets of different modality images.Subjective and objective evaluation on fused image indicates that the proposed method is better than a few existing typical fusion techniques based on multi-scale decomposition (MSD).关键词
平移不变不可分离剪切波变换/融合准则/非负矩阵分解/脉冲耦合神经网络Key words
translation invariance nonseparable shearlet transform/fusion rule/non-negative matrix factorization/pulse coupled neural network分类
信息技术与安全科学引用本文复制引用
才华,陈广秋,刘广文,耿朕野,杨勇..基于边界约束最优投影梯度NMF的TINST域图像融合方法[J].吉林大学学报（理学版）,2016,54(5):1087-1095,9.基金项目
教育部留学基金委留学归国人员科研启动基金(批准号:教外师留[第1685号])和吉林省科技发展计划项目(批准号:20130101179JC) （批准号:教外师留[第1685号]）
