Abstract:
Low-light image enhancement (LLIE) is important for various practical uses, aiming to improve
their visual quality. Deep learning advances have been the driving force behind recent development
in this field. Modern techniques utilize sophisticated neural networks to boost image
brightness, contrast, and reduce noise, representing the cutting edge of image processing and
computer vision. A new approach An Improved Deep Learning Model (AIDLM) is introduced,
designed to tackle the challenges of LLIE. AIDLM consists of three main modules: a retinex decomposition
network, an enhancement attention network, and a denoising network. The retinex
decomposition network uses an explicit parametric regularized Retinex model tailored to individual
pixels, while the enhancement attention network enhances the reflectance and illuminance
of the V-channel in the HSV color space through spatial and channel attention mechanisms
added to the UNet-like architecture , thus preventing color distortion. The denoising
module further cleans the enhanced RGB image by eliminating noise from the H and S channels.
Comprehensive experiments showed that AIDLM , greatly surpasses current baselines in
LLIE, providing higher image quality and robustness.
