Advancements in low light image enhancement techniques and recent applications

Abstract

Low-light image enhancement is an effective solution for improving image recognition by both humans and machines. Due to low illuminance, images captured in such conditions possess less color information compared to those taken in daylight, resulting in occluded images characterized by distortion, low contrast, low brightness, a narrow gray range, and noise. Low-light image enhancement techniques play a crucial role in enhancing the effectiveness of object detection. This paper reviews state-of-the-art low-light image enhancement techniques and their developments in recent years. Techniques such as gray transformation, histogram equalization, defogging, Retinex, image fusion, and wavelet transformation are examined, focusing on their working principles and assessing their ability to improve image quality. Further discussion addresses the contributions of deep learning and cognitive approaches, including attention mechanisms and adversarial methods, to image enhancement.