Thermal Image
Thermal imaging, capturing infrared radiation emitted by objects, offers valuable information complementary to visible light imagery, particularly in challenging conditions like low light or adverse weather. Current research focuses on improving thermal image analysis through deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs) for tasks such as object detection, scene reconstruction (including novel view synthesis using NeRFs), and image super-resolution. These advancements are significantly impacting diverse fields, including infrastructure inspection, autonomous driving, medical diagnostics, and agricultural monitoring, by enabling more robust and efficient solutions for various applications.
Papers
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-Resolution
Aditya Kasliwal, Pratinav Seth, Sriya Rallabandi, Sanchit Singhal
Thermal Spread Functions (TSF): Physics-guided Material Classification
Aniket Dashpute, Vishwanath Saragadam, Emma Alexander, Florian Willomitzer, Aggelos Katsaggelos, Ashok Veeraraghavan, Oliver Cossairt
Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition
Cedric Nimpa Fondje, Shuowen Hu, Benjamin S. Riggan
Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterization
Vasantha Ramani, Miguel Martin, Pandarasamy Arjunan, Adrian Chong, Kameshwar Poolla, Clayton Miller