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 …

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 …

Low-light images suffer from low contrast and unclear details, which not only reduces the
available information for humans but limits the application of computer vision algorithms …

Vision-based intelligent driving assistance systems and transportation systems can be
improved by enhancing the visibility of the scenes captured in extremely challenging …

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 …

This paper presents an optimization-based low-light image enhancement method using
spatially adaptive l 2-norm based Retinex model. The proposed method adaptively enforces …

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

This paper presents a dual autoencoder network model based on the retinex theory to
perform the low-light enhancement and noise reduction by combining the stacked and …

Image deraining is an important yet challenging image processing task. Though
deterministic image deraining methods are developed with encouraging performance, they …

Maritime video surveillance has become an essential part of the vessel traffic services
system, intended to guarantee vessel traffic safety and security in maritime applications. To …