Learning to See in the Dark

Understand

Imaging in low light is challenging due to low photon count and low SNR.

• Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical.
• A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night.
• To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images.