TDiff: Thermal Plug-And-Play Prior with Patch-Based Diffusion
This work addresses thermal image restoration for applications using low-cost cameras, but it is incremental as it adapts existing diffusion methods to a specific domain.
The paper tackles the problem of restoring low-quality thermal images from low-cost cameras by proposing a patch-based diffusion framework (TDiff) that trains on small patches to handle localized degradations, achieving strong results in denoising, super-resolution, and deblurring tasks on simulated and real data.
Thermal images from low-cost cameras often suffer from low resolution, fixed pattern noise, and other localized degradations. Available datasets for thermal imaging are also limited in both size and diversity. To address these challenges, we propose a patch-based diffusion framework (TDiff) that leverages the local nature of these distortions by training on small thermal patches. In this approach, full-resolution images are restored by denoising overlapping patches and blending them using smooth spatial windowing. To our knowledge, this is the first patch-based diffusion framework that models a learned prior for thermal image restoration across multiple tasks. Experiments on denoising, super-resolution, and deblurring demonstrate strong results on both simulated and real thermal data, establishing our method as a unified restoration pipeline.