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 …

Contrast enhancement and noise removal are coupled problems for low-light image
enhancement. The existing Retinex based methods do not take the coupling relation into …

Images captured in low-light conditions usually suffer from very low contrast, which
increases the difficulty of subsequent computer vision tasks in a great extent. In this paper, a …

Recently, low-light image enhancement has attracted much attention. However, some
problems still exist. For instance, sometimes dark regions are not fully improved, but bright …

Low-light image enhancement is an important challenge in computer vision. Most of the low-
light images taken in low-light conditions usually look noisy and dark, which makes it more …

Images captured under low-light conditions are typically prone to undesirable visual
phenomena, particularly color distortion and additive noise, that impede the aesthetics and …

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 …

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous
researches, many image enhancement methods have been proposed, but few methods can …

Low-light image enhancement aims to improve the quality of images captured under low-
lightening conditions, which is a fundamental problem in computer vision and multimedia …

Due to the absence of a desirable objective for low-light image enhancement, previous data-
driven methods may provide undesirable enhanced results including amplified noise …