双分支模型提升红外遥感图像超分辨率，兼顾细节与全局稳定。
Dual-Branch Remote Sensing Infrared Image Super-Resolution
- 采用Transformer局部修复与状态空间全局建模的双分支结构。
- 在12组合成数据上，融合结果在PSNR、SSIM和综合得分上均优于单分支。
- 适合需要高保真热成像重建的遥感与夜视应用场景。
遥感红外图像超分辨率旨在从低分辨率输入中恢复更清晰的热成像，同时保持目标轮廓、场景布局和辐射稳定性。与可见光图像超分辨率不同，热成像纹理稀疏且对局部锐化不稳定敏感，因此互补的局部与全局建模尤为重要。本文提出针对NTIRE 2026红外图像超分辨率挑战赛的解决方案，采用双分支系统：HAT-L分支与MambaIRv2-L分支。推理时对HAT进行测试时局部转换，对MambaIRv2进行八向自集成，最后以固定等权重在图像空间融合。我们在官方挑战评分及基于Caltech Aerial RGB-Thermal生成的12组倍率四倍合成热成像样本上进行了可复现评估，结果显示融合输出在PSNR、SSIM和综合得分上均优于任一单分支。结果表明，红外超分辨率受益于局部强Transformer恢复与全局稳定状态空间建模之间的显式互补性。
原文摘要 · Abstract (English)
Remote sensing infrared image super-resolution aims to recover sharper thermal observations from low-resolution inputs while preserving target contours, scene layout, and radiometric stability. Unlike visible-image super-resolution, thermal imagery is weakly textured and more sensitive to unstable local sharpening, which makes complementary local and global modeling especially important. This paper presents our solution to the NTIRE 2026 Infrared Image Super-Resolution Challenge, a dual-branch system that combines a HAT-L branch and a MambaIRv2-L branch. The inference pipeline applies test-time local conversion on HAT, eight-way self-ensemble on MambaIRv2, and fixed equal-weight image-space fusion. We report both the official challenge score and a reproducible evaluation on 12 synthetic times-four thermal samples derived from Caltech Aerial RGB-Thermal, on which the fused output outperforms either single branch in PSNR, SSIM, and the overall Score. The results suggest that infrared super-resolution benefits from explicit complementarity between locally strong transformer restoration and globally stable state-space modeling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。