Toward Physically Grounded Noise Modeling and Restoration for Uncooled Thermal Imaging

Uncooled microbolometer cameras provide compact and low-cost thermal imaging, but their measurements are dominated by complex noise fluctuations. Accurately decomposing these physical noise sources is inherently ill-posed, as many components are strongly correlated and cannot be uniquely identified from limited calibration observations. We propose an identifiability-aware framework for noise modeling and restoration in uncooled thermal imaging. Instead of estimating all physical noise terms, we identify a minimal set of calibration parameters from blackbody flat-field sequences, including per-pixel gain and offset, while temperature/state variation and unresolved correlated components are represented through calibrated perturbation and residual sampling. The remaining correlated components are modeled by residual sampling, enabling physics-consistent noise synthesis. This synthesis process allows restoration networks to be trained using paired data generated from calibrated sensor measurements. Experiments on two VOx microbolometer cameras demonstrate improved restoration quality across distinct camera operating regimes compared with traditional and learning-based restoration baselines. Additional validation using high-frame-rate sequences captured by a cooled LWIR camera and SAM-based segmentation further supports the realism of the synthesized noise and the effectiveness of the proposed pipeline.