EnGMHE: Enhanced Geometric Mean Histogram Equalization for Low-Light Image Enhancement

Low-light image enhancement has been extensively studied, with numerous methods proposed to address this challenge. Among these, Geometric Mean Histogram Equalization (GMHE) emerged as a histogram-based technique specifically designed for enhancing low-light images. Despite its effectiveness, GMHE has notable limitations: it often oversaturates results under specific conditions and amplifies noise, limiting its practical applicability. These shortcomings become particularly pronounced in real-world scenarios where low-light conditions are frequently accompanied by significant noise artifacts. To address these shortcomings, this study introduces EnGMHE, an enhanced version of GMHE. The proposed method consists of three key steps: 1) introducing a novel Gaussian Histogram Equalization (GHE) to improve image contrast and brightness, 2) utilizing GMHE to enhance sharpness and detail clarity, and 3) denoising the enhanced image using a pretrained deep neural network model. Together, these steps offer a more robust solution for low-light image enhancement, balancing contrast improvement, detail preservation, and noise reduction. The experimental results reveal not only the efficiency but also the effectiveness of the proposed model when benchmarked against the state-of-the-art methods.