Thermal Image Super-Resolution: Trends and Challenges

Thermal Image Super-Resolution has become pivotal for security, autonomous driving, industrial inspection, and surveillance. This work consolidates six editions of the TISR challenge held within the Perception Beyond the Visible Spectrum workshop at CVPR from 2020 to 2025, detailing the evolution of tasks, datasets, evaluation protocols, and participation. The analysis traces a methodological shift from convolutional neural networks to transformer-based and hybrid architectures that better capture long-range dependencies, besides the emergence of cross-spectral guidance that leverages visible imagery to enhance thermal detail at large scale factors. Quantitative trends in PSNR and SSIM across editions show consistent improvement in results. Remaining challenges include robust cross-spectral alignment, computational efficiency for resource-constrained deployment, broader dataset diversity across conditions, and resilience to noise and environmental variation. The synthesis provides a unified reference for benchmarking progress and outlines actionable directions for future advances in thermal image reconstruction.