## 3 Method
### Low-Light Image Enhancement Sub-Network  
In the realm of image illumination enhancement, the majority of research commonly employs methods like mapping curves or neural networks. However, this paper has the initiative to fit mapping curves with neural networks to design a low-light image enhancement sub-network. The objective was to homogenize the intensity distribution of the input image \(X_{T_{n}}\) from the nighttime target domain \(T_{n}\) and generate the enhanced image \(\hat{X}_{T_{n}}\), ensuring that the predictions of different domain samples align after passing through the segmentation network. Inspired by [14], we utilized an iterative pixel enhancement map  
Figure 1: Overall structure of the network proposed in this paper (DLA-Net). The network takes three types of domain-related samples as input source domain image \(X_{S}\) and target domain images \(X_{T_{d}}\) and \(X_{T_{n}}\). Within the framework, \(L_{S\to T_{d}}\) and \(L_{S\to T_{n}}\) are the adversarial losses of \(S\) and \(T_{d}\), while \(S\) and \(T_{n}\) are obtained from \(D_{S\to T_{d}}\) and \(D_{S\to T_{n}}\), respectively. ping curve to adjust the brightness and contrast of the image through the pixel grayscale mapping relationship, as shown in Equation (1). \[Ie(X_{T_{n}}(x);\alpha)=log(1+X_{T_{n}}(x)+\alpha X_{T_{n}}(x)(1-X_{T_{n}}(x))), \tag{1}\]  
where \(x\) is the pixel coordinate, and the \(\alpha\) parameter ensures that each pixel value in the enhanced image falls within the normalized range of \([0,1]\), preventing any loss of information due to overflow. By setting \(\alpha\) to a value between \(-1\) and \(1\), the \(Ie\) curve can be controlled within the range of \([0,1]\). For example, when \(\alpha=-1\), \(Ie(X_{T_{n}}(x);-1)=log(1+X_{T_{n}}(x)^{2})\), i.e., each value is within \([0,1]\). To adapt to more challenging low-light conditions, iterating the quadratic curve \(Ie\) could result in a higher-order curve. Although the higher-order curve is able to adjust the image over a wider area, it still applies a global adjustment as the \(\alpha\) value is applied to all pixels, resulting in over-enhancement or diminution of localized regions. To solve this problem, we used a separate curve for each RGB channel of the input image to perform an iterative transformation so that each channel has a corresponding optimal \(\alpha\) value for image enhancement, as shown in Equation (2). \[Ie_{m}(X_{T_{n}}(x);\mathcal{A})=log(1+Ie_{m-1}(X_{T_{n}}(x))+\mathcal{A}_{m}Ie _{m-1}(X_{T_{n}}(x))(1-Ie_{m-1}(X_{T_{n}}(x)))), \tag{2}\]  
where \(m\), set to \(8\) in this paper, signifies the number of iterations and controls the curvature, and \(\mathcal{A}\) is a parametric mapping with the same size as the given image used to represent the optimal \(\alpha\) value for each channel. To obtain the mapping relationship among the input image and its optimal curve parameter mapping, this paper proposes a depth curve fitting network, as illustrated in Figure 2.  
To evaluate the quality of the enhancement image, we used the following three losses to train the image enhancement network. To suppress overexposure or underexposure of certain areas, we designed an exposure control loss \(L_{cc}\) to regulate the level of exposure. \(L_{cc}\) quantifies the disparity between the mean luminance value of a specific area and the intended exposure level \(e\). \(e\) was set to a grayscale value in the RGB color space following existing methods [34; 35] in this paper. This loss brought the enhancement closer to the desired exposure level, mitigated overexposure or underexposure, and hence obtained a more visualized and higher-quality image, as shown in Equation (3). \[L_{cc}=\frac{1}{V}\sum_{i=1}^{V}|\hat{l}_{i}-e|, \tag{3}\]  
Figure 2: Architecture of the LIE-SubNet architecture. The network was designed to evaluate a set of optimal light enhancement curves (\(Ie\) curves) that iteratively enhance the input image.