The visual quality of photographs taken under imperfect lightness conditions can be
degenerated by multiple factors, eg, low lightness, imaging noise, color distortion, and so on …

Low-light image enhancement is challenging in that it needs to consider not only brightness
recovery but also complex issues like color distortion and noise, which usually hide in the …

Low-light image enhancement is important for high-quality image display and other visual
applications. However, it is a challenging task as the enhancement is expected to improve …

Recently, extensive research efforts have been made on low-light image enhancement.
Many novel models have been proposed, such as the ones based on the Retinex theory …

Low-light images have low brightness and contrast, which presents a huge obstacle to
computer vision tasks. Low-light image enhancement is challenging because multiple …

Low-light image enhancement is a very challenging problem due to insufficient or uneven
illumination, complicated noise and low contrast. Retinex-based methods have shown to be …

Low-light image enhancement aims to improve the illumination intensity while restoring
color information. Despite recent advancements using deep learning methods, they still …

Although a large number of methods have been proposed for low-light image enhancement,
there still remain challenges for these methods to simultaneously achieve excellent …

Low-light image enhancement algorithms can improve the visual quality of low-light images
and support the extraction of valuable information for some computer vision techniques …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic mappings from low-light to normally …