检索说明
以关键词【Image Fusion\ Infrared-Visible\ Infrared Image】在arXiv网站的文章标题中进行搜索,并将时间限定为04.01-04.30,共计检索10篇相关论文。包含:CVPR*2、CVPRW*1、TPAMI*1。
从整体结果来看,当前研究主要围绕红外--可见光融合、多光谱/高光谱融合以及融合与图像恢复的联合建模展开,尤其是在复杂退化场景(噪声、模糊、低分辨率)下的鲁棒融合问题受到广泛关注。同时,部分工作开始引入扩散模型、多任务学习以及闭环优化机制,推动融合方法从传统静态设计向更加灵活、自适应的方向发展。
从发展趋势上看,图像融合正逐步从单一任务建模走向"融合+恢复+感知"的统一框架,强调多模态信息的协同优化与端到端建模能力;与此同时,生成模型(如Diffusion)与数据驱动方法的引入,使得融合过程具备更强的表达能力与泛化潜力。未来,融合领域有望在全退化统一建模、动态自适应融合策略以及评价体系标准化等方向持续深入,并进一步与下游智能感知任务紧密结合,提升实际应用价值。


- Thermal background reduction for mid-infrared imaging by low-rank background and sparse point-source modelling
https://arxiv.org/abs/2604.22351v1


- The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
https://arxiv.org/abs/2604.21312v1


- CoFusion: Multispectral and Hyperspectral Image Fusion via Spectral Coordinate Attention
https://arxiv.org/abs/2604.10584v2


- Dual-Branch Remote Sensing Infrared Image Super-Resolution
https://arxiv.org/abs/2604.10112v2


https://arxiv.org/abs/2604.09030v1


https://arxiv.org/abs/2604.08924v1


https://arxiv.org/abs/2604.08922v1



- ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion
https://arxiv.org/abs/2604.05742v1


- EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment
https://arxiv.org/abs/2604.02896v1


- Harmonized Tabular-Image Fusion via Gradient-Aligned Alternating Learning
https://arxiv.org/abs/2604.01579v1
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