## 3 Methodology
### Backbone Network  
Our novel network structure, illustrated in Figure 5, builds upon the encoder-decoder design pioneered by the classic UNet [41], incorporating innovative adjustments in the input-output, encoder, and decoder layers. Distinctively, our network uses uniform noise matching the original image size as input to reduce dependency on specific training data and mitigate the risk of overfitting. In the encoder design, the network employs a series of convolutional modules. Each module is composed of two \(3\times 3\) convolutional layers to capture the local features. Following each convolutional layer, batch normalization (BN) is employed to maintain activation stability. To enhance the non-linearity of the network, a Leaky ReLU activation function follows each BN. Interspersed between these modules are down-sampling operations that progressively distill abstract features in the depth dimension. Echoing the encoder, the decoder part also includes multiple convolutional modules. However, its goal is to reconstruct the image from the deep features. For a more accurate recovery of image details at the spatial resolution, up-sampling operations connected between modules are implemented using \(3\times 3\) convolutions (also known as deconvolutions). Following every up-sampling, BN, and Leaky, ReLU activation functions are also used. Additionally, to fuse deep features with shallow statistical information, skip connections are introduced between the encoder and decoder at the same levels to minimize losses from up-sampling and down-sampling. At the end of the proposed network, we add a convolutional layer with an appropriate activation function, which reduces the 128 channels to \(N\) channels that are used to output weight maps \(\{s_{n}\}_{1}^{N}\). Importantly, the proposed weight map generation network can implicitly capture and utilize the hierarchical features. It effectively leverages the deep network's non-linear mapping capability to capture local features and map them onto pixel-level weight maps. This process enables the model to provide more stable and high-quality weight maps by utilizing the proposed loss function and image quality assessment metrics.  
In short, in this paper, we introduce a novel network structure based on UNet, which eliminates the need for prior knowledge in the construction of weight maps. Operating as a dynamic, non-linear mapping function, our network excels at autonomously generating custom pixel-wise weight maps for individual pre-enhanced images. The architecture of UNet, with its symmetrically organized contracting and expanding pathways, is specifically designed to capture contextual information while ensuring accurate localization. This design proves immensely advantageous for our application, enabling the network to  
Figure 4: Unsupervised weight map generative network for image generation. Initially, a low-light image is subjected to \(N\) distinct LLIE processes, producing pre-enhanced images \(\{\chi_{n}\}_{1}^{N}\). Thereafter, a specialized UNet architecture is utilized to generate local pixel-wise weight maps \(\{s_{n}\}_{1}^{N}\). These maps, alongside global weight coefficients \(\{W_{n}\}_{1}^{N}\), are instrumental in the iterative refinement of network parameters, governed by the multi-objective mixed loss function \(L_{\text{mul}}\). This process ultimately yields the final enhanced image, represented as \(\hat{s}_{i}\), encapsulating the integration of local and global mixed weighting methodologies.  
Figure 5: The architecture of the proposed UNet. The network’s initial stage involves receiving an input characterized by random noise, symbolized as \(\mathcal{Z}\). Simultaneously, a randomly generated output weight maps, \(\{s_{n}\}_{1}^{N}\), is produced, adhering to a configuration that mandates \(N\) channels for the output within the UNet framework.  
effectively learn intricate feature representations across different scales, which is crucial for accurately evaluating the importance of each pixel during the enhancement procedure.