UC Berkeley & Intel’s Photorealistic Denoising Method Boosts Video Quality on Moonless Nights

Compared to nocturnal animals that must hunt and forage in the dark to survive, humans have developed relatively weak night vision abilities, limiting our perception and environmental understanding under conditions such as moonless nights. While photographers can use long exposure times to collect sufficient light to represent static objects in low-light conditions, accurately capturing moving objects remains challenging due to the accumulation of camera noise that overwhelms and obscures the images.

In the new paper Dancing Under the Stars: Video Denoising in Starlight, a research team from UC Berkeley and Intel Labs leverages a GAN-tuned, physics-based noise model to represent camera noise under low light conditions and trains a novel denoiser that, for the first time, achieves photorealistic video denoising in starlight.

A sunny day will have an illumination level of about 100 kilolux, while moonlight will produce only about…