TDiff: Thermal Plug-And-Play Prior with Patch-Based Diffusion
Year: 2025
Type: conference-paper
Abstract: 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 (TD-iff ) that leverages the local nature of these distortions by training on small thermal patches. In this approach, fullresolution 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.
Authors: Piyush Dashpute, Niki Nezakati, Wolfgang Heidrich, Vishwanath Saragadam
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7090357397