Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network  

作　　者：YI Shi LIU Xi LI Li CHENG Xinghao WANG Cheng 

机构地区：[1]College of Mechanical and Electrical Engineering,Chengdu University of Technology,Chengdu 610059,China

出　　处：《Chinese Journal of Electronics》2023年第1期177-188,共12页电子学报（英文版）

基　　金：supported by the Open Foundation of Terahertz Science and Technology Key Laboratory of Sichuan Province(THZSC202001);Open Foundation of Key Laboratory of Industrial Internet of Things&Networked Control(2020FF06);Sichuan Science and Technology Program(2020YFG0458)。

摘　　要：The key to multi-sensor image fusion is the fusion of infrared and visible images.Fusion of infrared and visible images with generative adversarial network(GAN)has great advantages in automatic feature extraction and subjective vision improvement.Due to different principle between infrared and visible imaging,the blur phenomenon of edge and texture is caused in the fusion result of GAN.For this purpose,this paper conducts a novel generative adversarial network with blur suppression.Specifically,the generator uses the residual-in-residual dense block with switchable normalization layer as the elemental network block to retain the infrared intensity and the fused image textural details and avoid fusion artifacts.Furthermore,we design an anti-blur loss function based on Weber local descriptor.Finally,numerous experiments are performed qualitatively and quantitatively on public datasets.Results justify that the proposed method can be used to produce a fusion image with sharp edge and clear texture.

关 键 词：Image fusion Blur suppression generative adversarial network Weber local descriptor(WLD) Infrared image Visible image 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]