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 …

Global structure and local detailed texture have different effects on image enhancement
tasks. However, most existing works treated these two components in the same way, without …

Contrast enhancement and noise removal are coupled problems for low-light image
enhancement. The existing Retinex based methods do not take the coupling relation into …

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 …

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 …

Enhancing the quality of low-light (LOL) images plays a very important role in many image
processing and multimedia applications. In recent years, a variety of deep learning …

Low-light image enhancement based on deep convolutional neural networks (CNNs) has
revealed prominent performance in recent years. However, it is still a challenging task since …

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 …

The captured images under low-light conditions often suffer insufficient brightness and
notorious noise. Hence, low-light image enhancement is a key challenging task in computer …