TRNeRF: Restoring Blurry Rolling Shutter and Noisy Thermal Images with Neural Radiance Fields

Spencer Carmichael, Manohar Bhat, Mani Ramanagopal, Austin Buchan, Ram Vasudevan, Katherine A. Skinner; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7969-7979

Thermal cameras offer unique detection capabilities in building inspections search and rescue operations and autonomous vehicle perception. Of the different types of thermal cameras uncooled microbolometers are often chosen due to their relative affordability small size and low power consumption. However microbolometers suffer from motion blur rolling shutter distortions and fixed pattern noise which limit the conditions of their use. Nearly all prior methods for microbolometer image restoration account for only one of these degradations and current techniques addressing microbolometer blur and rolling shutter are limited. This paper presents TRNeRF a thermal image restoration method that jointly addresses all three degradations by incorporating the microbolometer image formation model with Neural Radiance Fields (NeRFs). To evaluate TRNeRF this paper introduces a new real-world dataset that is uniquely designed to support two novel quantitative evaluation strategies for thermal image restoration. Experiments demonstrate that TRNeRF is able to recover sharp global shutter and clear thermal images even under extremely aggressive camera motion that causes existing methods to fail. The code and dataset are available at: https://umautobots.github.io/trnerf.