  Low-light image enhancement is an important task in computer vision,
essential for improving the visibility and quality of images captured in
non-optimal lighting conditions. Inadequate illumination can lead to
significant information loss and poor image quality, impacting various
applications such as surveillance. photography, or even autonomous driving. In
this regard, automated methods have been developed to automatically adjust
illumination in the image for a better visual perception. Current enhancement
techniques often use specific datasets to enhance low-light images, but still
present challenges when adapting to diverse real-world conditions, where
illumination degradation may be localized to specific regions. To address this
challenge, the Adaptive Light Enhancement Network (ALEN) is introduced, whose
main approach is the use of a classification mechanism to determine whether
local or global illumination enhancement is required. Subsequently, estimator
networks adjust illumination based on this classification and simultaneously
enhance color fidelity. ALEN integrates the Light Classification Network
(LCNet) for illuminance categorization, complemented by the Single-Channel
Network (SCNet), and Multi-Channel Network (MCNet) for precise estimation of
illumination and color, respectively. Extensive experiments on publicly
available datasets for low-light conditions were carried out to underscore
ALEN's robust generalization capabilities, demonstrating superior performance
in both quantitative metrics and qualitative assessments when compared to
recent state-of-the-art methods. The ALEN not only enhances image quality in
terms of visual perception but also represents an advancement in high-level
vision tasks, such as semantic segmentation, as presented in this work. The
code of this method is available at https://github.com/xingyumex/ALEN.
