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.

翻译：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，同时保留目标轮廓、场景布局和辐射稳定性。与可见光图像超分辨率不同，热成像纹理较弱且对不稳定的局部锐化更敏感，这使得互补的局部与全局建模尤为重要。本文介绍了我们对NTIRE 2026红外图像超分辨率挑战赛的解决方案——一个结合HAT-L分支和MambaIRv2-L分支的双分支系统。推理流程对HAT采用测试时局部转换，对MambaIRv2采用八向自集成，并采用固定的等权图像空间融合。我们报告了官方挑战得分，以及基于加州理工学院航空RGB-热成像数据集的12个合成四倍热样本的可重复评估结果，其中融合输出在PSNR、SSIM和总体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。