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

In real scenes, due to the problems of low light and unsuitable views, the images often
exhibit a variety of degradations, such as low contrast, color distortion, and noise. These …

Current deep learning methods for low-light image enhancement (LLIE) typically rely on
pixel-wise mapping learned from paired data. However, these methods often overlook the …

Single channel speech enhancement has reached a great progress recently with the
development of deep learning. However, it is still a challenging problem to achieve …

In low-light environments, the amount of light captured by the camera sensor is reduced,
resulting in lower image brightness. This makes it difficult to recognize or completely lose …

As an emerging technology that has attracted huge attention, non-line-ofsight (NLOS)
imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface …

Recently, vision-based detection (VD) technology has been well-developed, and its general-
purpose object detection algorithms have been applied in various scenes. VD can be …

Low‐light image enhancement is a preprocessing work for many recognition and tracking
tasks for autonomous driving at night. It needs to handle various factors simultaneously …

Image denoising is a classic but still important issue in image processing as the denoising
effect has a significant impact on subsequent image processing results, such as target …

Low-light image enhancement based on deep learning has achieved breakthroughs
recently. However, the current methods based on deep learning have problems with …