Image Fusion

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)

NTIRE 2026 第三次恢复任何图像模型（RAIM）挑战：动态场景中的多曝光图像融合（Track 2）

Lishen Qu, Yao Liu, Jie Liang, Hui Zeng, Wen Dai, Guanyi Qin, Ya-nan Guan, Shihao Zhou, Jufeng Yang, Lei Zhang, Radu Timofte, Xiyuan Yuan, Wanjie Sun, Shihang Li, Bo Zhang, Bin Chen, Jiannan Lin, Yuxu Chen, Qinquan Gao, Tong Tong, Song Gao, Jiacong Tang, Tao Hu, Xiaowen Ma, Qingsen Yan, Sunhan Xu, Juan Wang, Xinyu Sun, Lei Qi, He Xu, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi

专题命中 Image Fusion ：image fusion(title,abstract)

AI总结 本文提出NTIRE 2026挑战，针对动态场景中多曝光图像融合的HDR成像难题，提供包含100训练序列和100测试序列的基准数据集，评估PSNR、SSIM、LPIPS等指标及感知质量，最终由114支队伍提交987份方案，提升去噪和细节恢复能力。

Comments Accepted by CVPRW 2026

Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion

定制融合：一种闭环动态网络用于自适应多任务感知红外-可见图像融合

Zengyi Yang, Yu Liu, Juan Cheng, Zhiqin Zhu, Yafei Zhang, Huafeng Li

专题命中 Image Fusion ：image fusion(title,abstract)

AI总结 本文提出闭环动态网络CLDyN，通过需求驱动语义补偿模块实现多任务自适应的图像融合，实验表明其在保持高质量融合的同时具备强多任务适应性。

Comments This paper has been accepted by CVPR 2026

Degradation-Robust Fusion: An Efficient Degradation-Aware Diffusion Framework for Multimodal Image Fusion in Arbitrary Degradation Scenarios

抗退化融合：一种高效的抗退化扩散框架，用于任意退化场景下的多模态图像融合

Yu Shi, Yu Liu, Zhong-Cheng Wu, Juan Cheng, Huafeng Li, Xun Chen

机构 * Department of Biomedical Engineering, Hefei University of Technology（合肥工业大学生物医学工程系） ； Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院） ； School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院） 纠错

专题命中 Image Fusion ：image fusion(title,abstract)

AI总结 本文提出一种高效的抗退化扩散框架，用于处理多模态图像融合中的复杂退化场景。该框架通过隐式去噪和联合观测模型校正机制，提升了在复杂退化下的融合性能。

Comments Accepted by CVPR 2026