Unsupervised Retinex Exposure Control: A Novel Approach to Image Enhancement

In domains such as autonomous driving and remote sensing, images often suffer from challenging lighting conditions, including low-light, backlighting and overexposure, which hinder the recognition of pedestrians, vehicles and traffic signs. While numerous methods have been proposed to address poor image exposure, they often struggle with images containing both low-light and overexposed regions. This paper presents an unsupervised learning-based exposure control method, providing a novel approach to improving image quality under diverse lighting conditions. Leveraging the inherent properties of Retinex theory, we introduce a novel yet simple formula that adjusts image exposure to produce visually pleasing results without requiring paired training data. Experiments on diverse image datasets validate the effectiveness of our approach in addressing various exposure challenges while preserving critical visual details. Our framework not only simplifies the exposure control process but also achieves state-of-the-art performance, highlighting its potential for real-world applications in computer vision and image processing.