LLCNN employs deeper convolutional neural networks specifically designed to capture and enhance features in low-light images. In comparison with LLNet, LLCNN not only achieves significant improvements in image quality but also demonstrates robust performance across various low-light conditions. Furthermore, [PERSON] et al. introduced Retinex-Net [19] to overcome the limitations of LLNet. Retinex-Net utilizes an end-to-end network architecture that learns illumination-aware decompositions, striking a balance between natural colors and details in LLIE. However, Retinex-Net often produces unnatural enhanced results. To address this issue, [PERSON] et al. proposed Kindling the Darkness (KinD) [20], which improved upon Retinex-Net by in troducing additional training losses and modifying the network architecture. However, KinD still faces challenges such as overexposure and halo artifacts. In response, the authors proposed KinD++ [21], which incorporates a multi-scale illumination attention module specifically designed to alleviate visual defects observed in KinD's results. Additionally, [PERSON] et al. addressed the limitation of deep Retinex-based methods, where noise is often neglected, by proposing a progressive Retinex network [22]. This approach consists of two interconnected subnetworks: the IM-Net, which estimates illumination, and the NM-Net, which assesses noise levels. These subnetworks collaboratively refine their outputs in a progressive manner until stable results are obtained. Overall, these supervision-based methods have significantly enhanced the quality of low-light images by continuously optimizing deep network architectures and improving training strategies. However, they rely on extensive paired data for supervised training, which can lead to model overfitting and limit their generalization capabilities. In unsupervised settings, EnlightenGAN [23] introduced an attention-equipped generator for image enhancement without the need for paired datasets. This approach aims to tackle the domain shift issue between training data and real-world applications. However, the stability of EnlightenGAN in extreme environments remains a challenge. To address the influence of unstable training on the enhancement process, [PERSON] et al. proposed Zero-DCE [24], a deep curve estimationJfor generating optimized pixel-level weighting maps. The generation process is guided by a multi-objective mixed loss function. To fuse the pre-enhanced images, we employ a mixed weighting mechanism that combines pixel-level weighting coefficients (referred to as weighting maps) and pre-defined global weighting coefficients for the entire pre-enhanced images. In the iterative generation of weighting maps, the iteration training process is terminated at appropriate steps based on the metric value of the no-reference image quality assessment algorithm, namely, NIQE [28]. The fused image obtained at this point serves as the final enhanced image. In summary, this research ingeniously combines the strengths of various LLIE methods, as validated by extensive experimental data. The effectiveness of this strategy stems from several innovative aspects:  
1. The proposed method takes a novel approach by maximizing the complementary advantages of different approaches in low-light image enhancement. Instead of relying solely on a single technique or method, it combines the strengths of multiple LLIE methods to ensure robust and versatile enhancement results across diverse scenes. This not only augments the quality of the final image but also significantly mitigates the common pitfalls associated with any single enhancement approach. Moreover, the integration of unsupervised training technique further refines the enhancement process, enabling the proposed method to handle a variety of lighting conditions, thus ensuring the generalizability of the enhancement and bringing it closer to a real-world adaptability. 2.