Abstract
Infrared video has been of great interest in visual tasks under challenging environments, but often suffers from severe atmospheric turbulence and compression degradation. Existing video super-resolution (VSR) methods either neglect the inherent modality gap between infrared and visible images or fail to restore turbulence-induced distortions. Directly cascading turbulence mitigation (TM) algorithms with VSR methods leads to error propagation and accumulation due to the decoupled modeling of degradation between turbulence and resolution. We introduce HATIR, a Heat-Aware Diffusion for Turbulent InfraRed Video Super-Resolution, which injects heat-aware deformation priors into the diffusion sampling path to jointly model the inverse process of turbulent degradation and structural detail loss. Specifically, HATIR constructs a Phasor-Guided Flow Estimator, rooted in the physical principle that thermally active regions exhibit consistent phasor responses over time, enabling reliable turbulence-aware flow to guide the reverse diffusion process. To ensure the fidelity of structural recovery under nonuniform distortions, a Turbulence-Aware Decoder is proposed to selectively suppress unstable temporal cues and enhance edge-aware feature aggregation via turbulence gating and structure-aware attention. We built FLIR-IVSR, the first dataset for turbulent infrared VSR, comprising paired LR-HR sequences from a FLIR T1050sc camera (1024 X 768) spanning 640 diverse scenes with varying camera and object motion conditions. This encourages future research in infrared VSR. Project page: this https URL
Abstract (translated)
红外视频在具有挑战性的环境下的视觉任务中引起了极大兴趣，但由于严重的大气湍流和压缩退化问题而受到限制。现有的超分辨率（VSR）方法要么忽略了红外图像与可见光图像之间的固有模式差异，要么无法恢复由湍流引起的失真。直接将湍流缓解(TM)算法与VSR方法串联会导致误差传播和累积，因为它们在建模湍流和分辨率退化之间没有耦合。 我们提出了HATIR（Heat-Aware Diffusion for Turbulent InfraRed Video Super-Resolution），一种热感知扩散技术，用于处理红外视频的湍流超分辨率问题。该方法将具有温度感知变形先验的热感知扩散注入到采样路径中，以联合建模湍流失真的逆过程和结构细节损失。 具体来说，HATIR构建了一个相量引导流动估计器（Phasor-Guided Flow Estimator），基于物理原理，即热活性区域在时间上表现出一致的相量响应。这使得能够可靠地感知湍流并指导反向扩散过程。为了确保在非均匀失真的情况下结构恢复的准确性，我们提出了一个具有湍流感知解码器（Turbulence-Aware Decoder），它可以选择性地抑制不稳定的时序线索，并通过湍流门控和结构感知注意机制增强边缘感知特征聚合。 为了支持未来的研究，我们建立了一个名为FLIR-IVSR的数据集，这是首个用于处理红外视频湍流超分辨率问题的专用数据集。该数据集包含从FLIR T1050sc相机（分辨率为1024 X 768）采集的一对低分辨率和高分辨率序列，包括跨越了640种不同场景的数据，涵盖了各种摄像机和物体运动条件。 这促进了未来在红外视频超分辨率领域的研究工作。项目页面：[请参阅原文链接]
URL
https://arxiv.org/abs/2601.04682
