  Low-light images, characterized by inadequate illumination, pose challenges
of diminished clarity, muted colors, and reduced details. Low-light image
enhancement, an essential task in computer vision, aims to rectify these issues
by improving brightness, contrast, and overall perceptual quality, thereby
facilitating accurate analysis and interpretation. This paper introduces the
Convolutional Dense Attention-guided Network (CDAN), a novel solution for
enhancing low-light images. CDAN integrates an autoencoder-based architecture
with convolutional and dense blocks, complemented by an attention mechanism and
skip connections. This architecture ensures efficient information propagation
and feature learning. Furthermore, a dedicated post-processing phase refines
color balance and contrast. Our approach demonstrates notable progress compared
to state-of-the-art results in low-light image enhancement, showcasing its
robustness across a wide range of challenging scenarios. Our model performs
remarkably on benchmark datasets, effectively mitigating under-exposure and
proficiently restoring textures and colors in diverse low-light scenarios. This
achievement underscores CDAN's potential for diverse computer vision tasks,
notably enabling robust object detection and recognition in challenging
low-light conditions.
