## 3 Methodology
### Basic Idea  
Our previous experiments have revealed the synergistic effects of various techniques in improving low-light images. Drawing inspiration from this observation, we devise a boosting strategy to effectively integrate the strengths of these complementary pre-enhanced images. Consequently, the main focus of this research centers around the task of image fusion. With regards to the problem of image fusion, we employ a hybrid weighted framework as follows:  
\[\hat{x}(i,j)=\sum_{n=1}^{N}W_{n}s_{n}(i,j)x_{n}(i,j), \tag{7}\]  
where \(N\) denotes the number of pre-enhanced images, and for each pixel at \((i,j)\), this framework combines \(N\) pre-enhanced images with the corresponding weighted value \(s_{n}(i,j)\) for the \(n\)-th pre-enhanced image, along with the global weight \(W_{n}\) specific to the \(n\)-th pre-enhanced image. By incorporating a combination strategy that combines global and local mixed weighting, this approach effectively preserves the texture details of each pre  
Figure 3: The enhanced results of different complementary methods on LOL-test dataset. (**a**) label; (**b**) Bread [39]; (**c**) R2R [40]; (**d**) SCI [38]; (**e**) Night [30]; (**f**) PairLIE [29]; (**g**) LLFlow [37]; (**h**) RetinexNet [19]; (**i**) EnlightenGAN [23]; (**j**) LIME [16]; (**k**) Ying [36]; (**l**) Zero-DCE [24]. enhanced image at a finer granularity and maximizes the advantages offered by different LLIEs. Having established this fundamental principle, the subsequent crucial question is about determining the LLIE methods that should be involved in the image fusion process. To comprehensively identify the optimal combination patterns, we select \(n\) mainstream LLIEs to process the same image. Subsequently, we evaluate the degree of complementarity among various combination patterns based on the quality of the fused image. Due to the intricate and complex nature of the pixel-level weight maps required for each pre-enhanced image, it becomes challenging to determine them through manual design. To overcome this challenge, we employ the classical UNet network, which is capable of generating the corresponding local weight maps at the pixel-level for each pre-enhanced image. By utilizing the UNet, we aim to preserve the texture details of each pre-enhanced image with finer granularity. To train the UNet, we adopt an unsupervised online iterative training mode. This training mode is guided by a specially designed loss function that facilitates iterative refinement. Through these iterations, the weights of the UNet are optimized to minimize the defined loss function, resulting in improved performance. Furthermore, we utilize the no-reference image quality assessment metric, NIQE, to measure the quality of the fused image. This metric allows us to evaluate the quality of the output image without requiring a reference image. By employing NIQE, we can determine the suitable iteration step to terminate the iterative training process. The fused image obtained at the \(l\)-th step serves as the final enhanced image, denoted as \(\hat{\kappa}_{l}\)  
The specific process is depicted in Figure 4. Initially, we take a low-light image \(y\) and apply \(N\) different LLIEs to enhance it, resulting in pre-enhanced images \(\{\chi_{n}\}_{1}^{N}\). Subsequently, we employ the proposed UNet network to generate local pixel-wise weight maps \(\{\hat{\kappa}_{n}\}_{1}^{N}\). These weight maps are combined with predefined global weight coefficients \(\{W_{n}\}_{1}^{N}\) to obtain the final enhanced image \(\hat{\kappa}_{l}\). To facilitate the iterative updates of network parameters, we propose a multi-objective mixed loss function. This loss function aims to preserve common elements across the pre-enhanced images, similar to a voting mechanism. By incorporating a combination strategy that utilizes both global and local mixed weighting, we ensure that the output fusion image achieves an approximate optimal quality.