Low-light image enhancement (LLIE) is a fundamental area of research with significant real-world applications. In fields such as autonomous driving, video surveillance, medical imaging, and night-time photography, capturing and analyzing high-quality images in low-light conditions is essential for accurate decision-making. However, images captured in such environments often suffer from poor visibility, noise, uneven exposure, and loss of detail, which can negatively impact the performance of computer vision algorithms. In this seminar several novel approaches to address these challenges using advanced deep-learning architectures that enhance low-light images efficiently and effectively shall be discussed.