## 3 Methods
### Zero-DCE Low-Light Enhancement Network Based on a Kernel Selection Module  
The low-light enhancement is disturbed by noise, which loses the local information around the noise, resulting in blurred images. To solve this problem, inspired by SKNet (Selective Kernel Networks) [15], we propose a Zero-DCE low-light enhancement network based on a kernel selection module. The low-light image is taken as the input, the curve parameter maps are learned using DCE-KSNet (Deep Curve Estimation Network Based on a Kernel Selection Module), and then the low-light image is adjusted at the pixel level by the luminance enhancement curves. An enhanced image is obtained after several iterations. The network framework is shown in Figure 5.  
The luminance enhancement curve is:  
\[LE_{n}(x)=LE_{n-1}(x)+A_{n}(x)LE_{n-1}(x)(1-LE_{n-1}(x)) \tag{11}\]  
where \(A_{n}\) signifies the curve parameter map with a size equal to the input image, \(x\) denotes the image pixel coordinates, and \(n\) denotes the number of iterations. After the above equation, each pixel of the input image is endowed with an optimal higher-order curve that enables it to dynamically adjust its brightness. DWConv (depthwise separable convolution) [16] is used in place of a regular convolution in DCE-Net (Deep Curve Estimation Network) in order to decrease the number of parameters and computing effort. The deep convolution block separately convolves each channel to extract the data for a single channel and then extends or compresses the input feature maps' channels by a \(1\times 1\) point-by-point convolution block to produce feature  
Figure 5: Zero-DCE low-light enhancement network framework based on a kernel selection module. maps of the desired size. The deep separable convolution kernel can drastically reduce the number of parameters in the network compared with a regular convolution kernel while essentially maintaining network accuracy. Noise interference can be reduced by fully utilizing spatial features and using different scales of receptive fields for multi-scale fusion. In terms of multi-scale feature fusion, most of the existing methods are based on a feature pyramid structure, combining features by way of an element addition or a series connection, which ignores the spatial and channel specificities of the features of different scales, although it can combine feature maps of different scales. A three-branch kernel selection module is added, as shown in Figure 5, after the 7 th convolutional layer of DCE-Net to adaptively adjust the receptive field size, dynamically select the appropriate path, and reduce the impact of noise on the low-light enhancement. Convolution kernels of sizes 3, 5, and 7 are used to process the input feature map \(U\) to produce the output feature maps \(U^{\prime}\), \(U^{\prime\prime}\), and \(U^{\prime\prime\prime}\), with the \(5\times 5\) convolution kernel being made up of two \(3\times 3\) dilated convolutions. The three are then added to produce \(\widetilde{U}\) to integrate the data from all branches. \(\widetilde{U}\) is embedded into the global information \(s\) by GAP (Global Average Pooling). \[s_{c}=F_{gap}(\widetilde{U}_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{ W}\widetilde{U}_{c}(i,j) \tag{12}\]  
The dimensions of the feature map are \(H\) and \(W\); \(s\) is then passed through the fully connected layer to produce a compact feature map \(z\in R^{d\times C}\):  
\[z=F_{fc}(s)=\delta(B(W_{s})) \tag{13}\]  
where \(B\) is the batch normalization process, \(\delta\) is the RELU activation function, and \(W\in R^{d\times C}\) and \(d\) values are controlled by the compression ratio \(r\):  
\[d=max\big{(}\frac{C}{r},L\big{)} \tag{14}\]  
where \(L\) is the minimum value of \(d\), generally taken as \(L=32\). In order to obtain weights at different spatial scales and, thus, the weighted fusion information for different sensory fields, a _Softmax_ operation in the direction of the vector \(z\)-channel is obtained:  
\[