Images captured in low-light conditions typically suffer from reduced visibility, poor contrast, and excessive noise, making it challenging to extract meaningful details. These issues significantly impact applications such as surveillance, medical imaging, and autonomous systems, where clear and high-quality images are essential for accurate decision- making. Traditional enhancement techniques aim to improve brightness and contrast but frequently introduce artifacts or fail to adapt to varying lighting conditions. This research explores an advanced approach to low-light image enhancement that focuses on improving visibility while preserving natural details. By analyzing the structural and illumination properties of images, the proposed method enhances brightness, reduces noise, and maintains color consistency. The model is designed to adaptively enhance different regions of an image, ensuring a balanced improvement in both dark and bright areas. Experimental evaluations demonstrate that the proposed approach effectively enhances image clarity without over-amplifying noise or distorting details. Comparative analysis with conventional methods highlights its superior performance in producing visually appealing results suitable for real-world applications. This study contributes to the ongoing advancements in image processing by providing an efficient and adaptive solution for enhancing images captured in challenging lighting conditions.
Chandu et al. (Wed,) studied this question.