FaithIR：从感知锐度到任务相关保真度重新思考红外图像超分辨率
FaithIR: Rethinking Infrared Image Super-Resolution from Perceptual Sharpness to Task Relevant Fidelity
中文总结 AI 辅助
针对现有红外图像超分辨率方法易扭曲结构与语义信息的问题，提出FaithIR框架，在像素域结合全局与局部分支实现忠实重建，提升了跨数据集泛化及下游任务性能。
中文摘要 AI 辅助
红外图像超分辨率（IISR）对目标检测、语义分割等下游任务至关重要。现有IISR方法常产生人工纹理、过锐边缘和虚假高频细节，扭曲真实热结构与语义信息。为解决该问题，本文提出FaithIR——一种用于可靠机器感知的忠实红外超分辨率框架。FaithIR由捕捉全局热与结构信息的 patch-level 条件分支，及在结构引导下执行密集局部重建的像素级恢复分支构成。整个恢复过程直接在像素域进行，以保留红外特有结构与任务相关信息。在FLIR-IISR、M3FD和FMB数据集上开展的大量实验表明，该方法具备强大的重建保真度、跨数据集泛化能力，且在目标检测和语义分割任务中表现优异。这些结果表明，保留忠实的红外结构对可靠机器感知而言，比单纯追求感知锐度更为重要。
英文摘要
Infrared image super-resolution (IISR) is important for downstream tasks such as object detection and semantic segmentation. Existing IISR methods often produce artificial textures, over-sharpened edges, and spurious high-frequency details that distort authentic thermal structures and semantic information. To address this issue, we propose FaithIR, a faithful infrared super-resolution framework for reliable machine perception. FaithIR consists of a patch-level conditioning branch that captures global thermal and structural information and a pixel-level restoration branch that performs dense local reconstruction under structural guidance. The entire restoration process is performed directly in the pixel domain to preserve infrared-specific structures and task-relevant information. Extensive experiments on FLIR-IISR, M3FD, and FMB demonstrate strong reconstruction fidelity, cross-dataset generalization, and superior performance in object detection and semantic segmentation. These results show that demonstrate that preserving faithful infrared structure preservations is more important for reliable machine perception than merely pursuing perceptual sharpness alone.
