北京航空航天大学学报2026，Vol.52Issue(4)：1107-1115,9.DOI:10.13700/j.bh.1001-5965.2024.0072
视觉显著性增强的双鉴别器红外与可见光图像融合
Dual discriminator fusion of infrared and visible light images for visual saliency enhancement
摘要
Abstract
In order to solve the problem of unclear edges and missing details in infrared and visible light image fusion,a saliency enhanced dual discriminator generation adversarial infrared and visible light image fusion method is proposed.First,infrared and visible light images are broken down using anisotropic diffusion,while visible light images are improved using local adaptation.Then,visual saliency detection is used to visually enhance the decomposed detail layer image and the base layer image.Next,a dense connected DenseNet generator model is designed to perform feature learning on visually enhanced images.Finally,the fusion result is obtained by competing with the dual discriminator game.Experimental results demonstrate that the suggested approach has more precise information and performs better than the comparison algorithm in both subjective and objective assessments when compared to ten fusion techniques in a public dataset.Compared with the FusionGAN algorithm,the proposed method has improved objective evaluation indicators such as information entropy,spatial frequency,structural similarity,and standard deviation by 7.4%,58.8%,25.5%,and 35.7%,respectively.关键词
红外与可见光图像融合/视觉显著性增强/各向异性扩散/双鉴别器/生成对抗网络Key words
infrared and visible light image fusion/visual saliency enhancement/anisotropic diffusion/dual-discriminator/generate adversarial network分类
信息技术与安全科学引用本文复制引用
陈永,周方春,董珂..视觉显著性增强的双鉴别器红外与可见光图像融合[J].北京航空航天大学学报,2026,52(4):1107-1115,9.基金项目
国家自然科学基金(62462043,61963023) （62462043,61963023）
甘肃省自然科学基金(26JRRA589) National Natural Science Foundation of China(62462043,61963023) （26JRRA589）
Gansu Provincial Nature Science Foundation(26JRRA589) （26JRRA589）
