Temporal super-resolution with a latent diffusion model for optically measured sound field

This paper proposes a latent-diffusion-model-based method to achieve temporal super-resolution in optical sound-field imaging. Optical sound-field imaging, known for its high spatial resolution, measures sound by detecting small variations in the refractive index of air caused by sound. Due to the extremely high speed of sound propagation, optical imaging systems require exceptionally high frame rates for sound-field measurement, which results in high power consumption as well as extremely time-consuming data transmission and processing during long-duration data acquisition. Therefore, to alleviate the sampling requirements of high-speed cameras, mitigate data transmission and processing demands, reduce imaging power consumption, and enhance the temporal quality of imaging, we propose a latent-diffusion-model-based method to achieve temporal super-resolution. During inference, the measured sound-field images are utilized as conditional constraints, enabling the model to generate intermediate sound-field frames between any two consecutive frames in the temporal sequence. Numerical experiments demonstrate that our method outperforms other deep learning approaches in the temporal super-resolution task on optically measured sound-field data. The experimental results demonstrate the applicability of our model to the real world.