Dynamic Fusion for Generating High-Quality Labels in Low-Light Image Enhancement

Abstract

Generating high-quality labels is crucial for self-supervised learning in low-light conditions, where traditional enhancement methods often struggle to balance detail enhancement and color fidelity. This paper presents a traditional image fusion approach that dynamically combines Multi-Scale Retinex (MSR) and Adaptive Histogram Equalization (AHE) outputs with the original image using an adaptive weighting strategy. The primary goal is not to compete with state-of-the-art deep learning-based enhancement methods but to produce intermediate images that can serve as effective labels for training self-supervised models without requiring ground-truth datasets. By dynamically fusing MSR and AHE outputs with the original image using adaptive brightness and color weights, the method improves structural integrity while enhancing brightness and color consistency. Experiments on standard low-light datasets demonstrate significant improvements in PSNR and SSIM compared to traditional enhancement methods. However, a visual analysis of the generated labels reveals differences in color saturation when compared to ground truth, providing insights into designing a suitable loss function for future self-supervised learning applications. It is important to note that this work does not include experiments or methods related to self-supervised learning itself; instead, it focuses on preparing high-quality labels for such approaches. Additionally, our method strikes a balance between computational efficiency and visual quality, making it suitable for real-time applications and paving the way for more robust and versatile learning frameworks.