Images acquired in low-light situations generally suffer from subjective visual effects on human eyes and the performance of different machine vision systems due to low brightness, low contrast, a limited gray range, color distortion, and high noise. Enhancing photographs taken in low light is done so that they may be used in other applications more effectively. In this study, we look back at some of the most important methods for improving images in dim light. We categorize these techniques into seven distinct groups: gray transformation, histogram equalization, Retinex, frequency-domain, picture fusion, defogging model, and machine learning. Methods are presented across the board, from the broadest to the most specific categories, emphasizing the underlying concepts and distinguishing features. Different improved picture quality assessment techniques are also described, as are comparisons of the various algorithms used. Finally, a state-of-the-art summary and recommendations for where the field should go are presented.