典型文献
SSL-WAEIE:Self-Supervised Learning With Weighted Auto-Encoding and Information Exchange for Infrared and Visible Image Fusion
文献摘要：
Dear editor,
Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual tasks such as segmentation and object detection[2].
Traditional fusion methods mainly include spatial domain-based methods and multi-scale decomposition-based(MSD)methods.The former ones,such as the guided filter-based methods(GF)[3]and Bayesian[4],are to produce fusion images generally by weighting local pixels or saliency of source images.MSD methods,including TE-MST[5],Hybrid-MSD[6],MDLatLRR[7],etc.,first decompose the source images into multi-scale features,and further employ fusion rules to integrate these features at each level,for a recon-structed result.However,how to pixel-wisely measure the impor-tance or fusion contribution of source images is always an open problem in these methods.Such they must elaborately design reasonable weighed strategies or fusion rules.
文献关键词：
中图分类号：
作者姓名：
Gucheng Zhang;Rencan Nie;Jinde Cao
作者机构：
School of Information Science and Engineering,Yunnan University,Kunming 650500,China;School of Information Science and Engineering,Yunnan University,Kunming 650500;Yunnan Key Laboratory of Intelligent Systems and Computing,Kunming 650500;School of Automation,Southeast University,Nanjing 210096,China;School of Mathematics,Southeast University,Nanjing 210096,China;Yonsei Frontier Laboratory,Yonsei University,Seoul 03722,South Korea
文献出处：
引用格式：
[1]Gucheng Zhang;Rencan Nie;Jinde Cao-.SSL-WAEIE:Self-Supervised Learning With Weighted Auto-Encoding and Information Exchange for Infrared and Visible Image Fusion)[J].自动化学报（英文版）,2022(09):1694-1697
A类：
WAEIE,
Infrared,
Traditional,MDLatLRR,wisely
B类：
SSL,Self,Supervised,Learning,With,Weighted,Auto,Encoding,Information,Exchange,Visible,Image,Fusion,Dear,editor,visible,fusion,IVIF,technologies,extract,complementary,information,from,source,images,generate,single,fused,result,which,widely,applied,various,high,level,visual,tasks,such,segmentation,object,detection,methods,mainly,include,spatial,domain,multi,scale,decomposition,MSD,former,ones,guided,filter,GF,Bayesian,produce,generally,by,weighting,local,pixels,saliency,including,TE,MST,Hybrid,etc,first,decompose,into,features,further,employ,rules,integrate,these,each,recon,structed,However,how,measure,impor,tance,contribution,always,open,problem,Such,they,must,elaborately,design,reasonable,weighed,strategies
AB值：
0.632824
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