Low-light images are not conducive to human observation and computer vision algorithms
due to their low visibility. To solve this problem, many image enhancement techniques have …

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 …

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 …

Image enhancement is a fundamental low-level task of significant importance that can
directly affect high-level image processing tasks. Although various methods have been …

The rising demand for high quality display has ensued active research in high dynamic
range (HDR) imaging, which has the potential to replace the standard dynamic range …

Low light image enhancement algorithms intent to produce visually pleasant images and
target to extract valuable information for computer vision applications. The task of improving …

Although agreement between the annotators who mark feature locations within images has
been studied in the past from a statistical viewpoint, little work has attempted to quantify the …

Given the visual resemblance between inverted low-light and hazy images, dehazing
principles are borrowed to enhance low-light images. However, the essence of such …

Current low-light image enhancement methods have made great progress on improving the
visibility of low-light images. Nevertheless, they pay less attention to preserving visual …

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 …