Real-world low-light image enhancement using Variational Autoencoders
Low-light image enhancement is a hard task mainly due to the amount of noise and little information stored in the dark image. In this thesis project we develop a method for low-light image enhancement based on a Conditional Variational Autoencoder (CVAE). The CVAE is a deep learning model trained with a specific objective function (ELBO). A CVAE can be implemented as a variant of a U-Net utilizing