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观测结果，同时保持目标轮廓、场景布局及辐射稳定性。与可见光图像超分辨率不同，热成像纹理较弱，且对局部锐化不稳定更为敏感，因此互补的局部与全局建模尤为关键。本文介绍了我们针对NTIRE 2026红外图像超分辨率挑战赛所提出的解决方案：一种融合HAT-L分支与MambaIRv2-L分支的双分支系统。推理流程在HAT分支上应用测试时局部变换，在MambaIRv2分支上采用八路自集成，并在图像空间进行固定等权融合。我们在官方挑战赛评分之外，还在由Caltech Aerial RGB-Thermal数据集生成的12幅合成×4倍热图像样本上进行了可复现评估；融合输出在PSNR、SSIM及总体Score指标上均优于任一分支单独结果。结果表明，红外超分辨率可得益于局部强表达能力的Transformer重建与全局稳定的状态空间建模之间的显式互补性。

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.