Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Images captured under poor illumination conditions often exhibit characteristics such as low
brightness, low contrast, a narrow gray range, and color distortion, as well as considerable …

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure
images suffer from noise, while long exposure can lead to blurry images and is often …

In surveillance, monitoring and tactical reconnaissance, gathering visual information from a
dynamic environment and accurately processing such data are essential to making informed …

Deep learning has recently been applied with impressive results to extreme low-light
imaging. Despite the success of single-image processing, extreme low-light video …

In an environment with poor illumination, such as indoor, night, and overcast conditions, the
image information can be seriously lost, which affects the visual effect and degrades the …

We propose automatic contrast-limited adaptive histogram equalization (CLAHE) for image
contrast enhancement. We automatically set the clip point for CLAHE based on textureness …

In the low-light enhancement task, one of the major challenges lies in how to balance the
image enhancement properties of light intensity, detail presentation and color fidelity. In …

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 …

Image captured under poor-illumination conditions often display attributes of having poor
contrasts, low brightness, a narrow gray range, colour distortions and considerable …