Underexposed Image

Underexposed images, characterized by insufficient light, pose significant challenges in various applications, from medical imaging to high-speed photography. Current research focuses on developing deep learning-based methods, often employing neural networks and implicit representations, to enhance these images by recovering lost detail and improving overall visual quality. These techniques are evaluated through metrics like image quality and downstream task performance (e.g., hand pose estimation), with a strong emphasis on robustness across diverse and challenging scenarios. Improvements in underexposed image enhancement have significant implications for numerous fields, enabling better analysis of low-light imagery and improving the accuracy of computer vision systems.