To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the
mapping relationship between them is one-to-many. Previous works based on the pixel-wise …

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of
an image captured in an environment with poor illumination. Recent advances in this area …

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 …

Images captured in weak illumination conditions could seriously degrade the image quality.
Solving a series of degradation of low-light images can effectively improve the visual quality …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic mappings from low-light to normally …

In this paper, we present a systematic review and evaluation of existing single-image low-
light enhancement algorithms. Besides the commonly used low-level vision oriented …

Low-light image enhancement is challenging in that it needs to consider not only brightness
recovery but also complex issues like color distortion and noise, which usually hide in the …

Deep neural networks have achieved remarkable progress in enhancing low-light images
by improving their brightness and eliminating noise. However, most existing methods …

Image enhancement plays an important role in improving image quality in the field of image
processing, which is achieved by highlighting useful information and suppressing redundant …

It has been widely acknowledged that under-exposure causes a variety of visual quality
degradation because of intensive noise, decreased visibility, biased color, etc. To alleviate …