{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/thermal-image-processing-via-physics-inspired","title":"Thermal Image Processing via Physics-Inspired Deep Networks","arxiv_id":"2108.07973","date":"2021-08-18","proceeding":null,"authors":["Vishwanath Saragadam","Akshat Dave","Ashok Veeraraghavan","Richard Baraniuk"],"abstract":"We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (that can be accurately modeled using physics) and a scene-specific radiance flux (that is well-represented using a deep network-based regularizer). DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target--making it well suited for practical computer vision tasks. We demonstrate the power of going DeepIR by developing new denoising and super-resolution algorithms that exploit multiple images of the scene captured with camera jitter. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images, achieving a 10dB PSNR improvement over competing approaches.","url_abs":"https://arxiv.org/abs/2108.07973v2","url_pdf":"https://arxiv.org/pdf/2108.07973v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"thermal-image-processing-via-physics-inspired","repo_url":"https://github.com/vishwa91/deepir","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"sensor-modeling","task_name":"Sensor Modeling"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"deepir","method_name":"DeepIR"}],"datasets_introduced":[],"methods_introduced":[{"slug":"deepir","name":"DeepIR","full_name":"DeepIR"}],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}