Improved Classical and Novel Methods for Low-Light Image Enhancement

Image enhancement in low-light conditions has gained significant attention in recent years due to its importance in improving visual clarity and uncovering hidden details in poorly illuminated images. This study focuses on the application of classical methods for enhancing color and brightness, as these approaches provide a practical balance between efficiency and performance compared to computationally intensive modern techniques. Classical algorithms were applied, modified, and extended to develop lightweight solutions capable of improving image quality through relatively simple processes. In this work, a set of classical enhancement methods was tested on images captured in night conditions. Performance was evaluated using metrics such as the Structural Similarity Index Measure (SSIM) and the Signal-to-Noise Ratio (SNR), which assess improvements in brightness, color quality, and detail preservation. Additionally, three novel techniques were proposed: enhanced Hue, Saturation, Value (HSV) through scaling of the Value channel, Custom HSV-based Brightness and Saturation Scaling, and an Entropy- based Hybrid Enhancement combining HSV and Lab* color spaces. Results showed that the proposed methods outperformed conventional histogram equalization, with the best-performing approach achieving up to a 41.4% improvement in Structural Similarity Index Measure. Overall, the findings demonstrate that classical and improved lightweight methods remain effective and computationally efficient for low-light image enhancement.