AI 中文总结
针对现有热纹理成像方法的缺陷，提出T²exture框架，通过两阶段重建实现稀疏主动采集成像下的热纹理序列，在模拟基准上参数增量小且PSNR提升显著，纹理与结构恢复效果优于VFI基线。
AI 中文摘要
热成像在恶劣光照下仍能发挥作用，但被动长波红外（LWIR）测量通常缺乏精细纹理。现有热纹理成像方法通常依赖光谱传感或配准的辅助模态，会产生大量数据吞吐量或易受跨模态退化影响。我们提出T²exture，一种稀疏扰动热纹理成像框架，旨在从密集采样的被动帧和少量主动扰动关键帧中重建时间密集的热纹理序列。我们将热纹理定义为光源开启观测值与其对应光源关闭被动状态之间的残差。在稀疏LWIR光照和快速准稳态配对采集下，该残差会衰减被动发射背景并近似光源诱导的反射响应，凸显依赖局部材料和几何的纹理。T²exture通过两个阶段重建该光源条件响应的密集序列：第一阶段从相邻被动帧估计每个主动时刻未观测的光源关闭被动状态，以获得可靠的差分纹理锚点；第二阶段将稀疏锚点与每个目标时刻附近的被动结构上下文结合，重建密集序列。在模拟基准上，T²exture仅为AMT-L增加0.20M参数，同时将峰值信噪比（PSNR）提高6.66 dB。对模拟和真实采集的广泛评估进一步表明，与代表性视频帧插值（VFI）基线相比，T²exture实现了更清晰的纹理恢复和更强的结构保留。这些结果确立了T²exture作为稀疏主动采集成像下实用热纹理成像框架的地位。
英文摘要
Thermal imaging remains effective under adverse illumination, yet passive long-wave infrared (LWIR) measurements often lack fine texture. Existing thermal texture imaging approaches commonly rely on spectral sensing or registered auxiliary modalities, incurring substantial data throughput or vulnerability to cross-modal degradation. We introduce T$^2$exture, a sparsely perturbed thermal texture imaging framework that aims to reconstruct temporally dense thermal texture sequences from densely sampled passive frames and a few actively perturbed keyframes. We define thermal texture as the residual between a source-on observation and its corresponding source-off passive state. Under sparse LWIR illumination and rapid quasi-steady paired acquisition, this residual attenuates the passive-emission background and approximates a source-induced reflected response, exposing localized material- and geometry-dependent texture. T$^2$exture reconstructs a dense sequence of this source-conditioned response through two stages. Stage 1 estimates the unobserved source-off passive state at each active instant from neighboring passive frames to obtain reliable differential texture anchors. Stage 2 combines sparse anchors with passive structural context near each target time to reconstruct the dense sequence. On the simulated benchmark, T$^2$exture adds only 0.20M parameters to AMT-L while improving PSNR by 6.66 dB. Extensive evaluations on simulated and real acquisitions further show clearer texture recovery and stronger structural preservation than representative VFI baselines. These results establish T$^2$exture as a practical framework for thermal texture imaging under sparse active acquisition.
Comments13 pages, 7 figures
