This work focuses on the extremely low-light image enhancement, which aims to improve
image brightness and reveal hidden information in darken areas. Recently, image …

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 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 …

Images obtained under low-light conditions are usually accompanied by varied and highly
unpredictable degradation. The uncertainty of the imaging environment makes the …

Low-light images incur several complicated degradation factors such as poor brightness,
low contrast, color degradation, and noise. Most previous deep learning-based approaches …

Images captured in low-light environments suffer from serious degradation due to insufficient
light, leading to the performance decline of industrial and civilian devices. To address the …

Images captured under low-light environments typically have poor visibility, affecting many
advanced computer vision tasks. In recent years, there have been some low-light image …

Recent approaches for low-light image enhancement achieve excellent performance
through supervised learning based on convolutional neural networks. However, it is still …

We present a deep learning based method for low-light image enhancement. This problem
is challenging due to the difficulty in handling various factors simultaneously including …

Low-light image enhancement focus on refining the illumination and keep naturalness to
obtain the normal-light image. Current low-light image enhancement methods can well …