Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …

No-Reference (NR) quality assessment is a crucial approach for evaluating the quality of low-
light enhanced images, as it is often difficult to acquire high-quality reference images in …

Images captured under poor lighting conditions often suffer from low brightness, low
contrast, color distortion, and noise. The function of low-light image enhancement is to …

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or
back-lighting conditions. However, LIEA may introduce various distortions such as structure …

Low-light is a challenging environment for image processing and computer vision tasks,
either in contrast enhancement for better visibility and quality, or application oriented tasks …

This study introduces a low-light image enhancement method using a hybrid deep-learning
network and mixed-norm loss functions, in which the network consists of a decomposition …

Low-light images, which are usually taken in dark or back-lighting conditions, are hard to
perceive due to the low visibility and low contrast. To improve viewers' Quality of Experience …

Low-light image enhancement aims to improve the perceptual quality of images captured in
conditions of insufficient illumination. However, such images are often characterized by low …

Images captured in low-brightness environments often lead to poor visibility and exhibit
artifacts such as low brightness, low contrast, and color distortion. These artifacts not only …

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