# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 3 Proposed Method
### Intensity Component Enhancement  
Literature [20] suggests that learning the mapping relationship between rainy image and rain (i.e., residual of the rainy image and clean image) can reduce the pixel range of the mapping and make the learning process easier. Inspired by this knowledge, we do not directly use the deep convolution neural network to train and learn the end-to-end mapping relationship between low-light RGB image and normal illumination RGB image, but we do learn the mapping of the intensity component between low-light image and normal illumination image in HSI space. As for RGB, it still needs to consider the color changes during training, we use HSI color space and achieve more conducive of network training. So, this model inputs the intensity component of the low-light image and outputs the enhanced intensity component. Network Architecture  
Unlike traditional convolution neural network, our model called DCNN used to enhance intensity has no pooling layer and fully-connected layer. According to different functions, five parts can be considered; input, feature extraction, nonlinear mapping, reconstruction, and output. As seen in Figure 5, feature extraction is mainly used for the edge texture and other structural features of image, whereas the non-linear mapping basically targets the high-light region first and then the low-light region. The network model requires that the network output and input image have the same size. But both the pooling layer and the convolution operation make the size become smaller. That means, the original image greatly loses information. Therefore, zero-padding operatio  
Figure 5: The network structure of DCNN for enhancing intensity. all the convolution layers, which guarantee the image size during the forward transmission of the network is not changing at all. In following, the five parts mentioned in the network are explained in detail. (1) Input and output: To facilitate better training of the network, neither the input nor output of the proposed network is a whole image. In contrast, patches of low-light image and corresponding normal illumination image are randomly selected as training samples. At the same time, the used model only enhances the intensity component, so the input is the intensity component of the low-light image and the output is the enhanced intensity component. (2) Feature extraction: The first part of this network is designed for feature extraction and made up of convolution layer. The convolution layer is often used to extract the features of different local regions by convolution operation between several learnable filters and input image or upper layer feature maps and the nonlinear activation function. Here, we use \(H_{i}\) to represent the first \(i\) layer feature map of the convolutional neural network; suppose the input image patch of the network is \(\mathbf{X}\) and the feature extraction function is expressed as,  
\[H_{1}(\mathbf{X})=max(0,\mathbf{W}_{1}*\mathbf{X}+\mathbf{b}_{1}) \tag{7}\]  
where \(\mathbf{W}_{1}\) denotes the filters, \(\mathbf{b}_{1}\in\mathbf{R}^{n_{1}}\) represents the bias vector, and \(\mathbf{X}\) is a matrix with size \(n\times n\). Suppose \(f_{1}\) with size of a single filter and \(c\) as the number of channels in the input image (we explained that only the intensity component is processed, so \(c=1\)), if there are \(n_{1}\) filters, then the size of \(\mathbf{W}_{1}\) is \(f_{1}\times f_{1}\times n_{1}\times c\). (3) Nonlinear mapping: In this paper, nonlinear mapping consists of several convolutional module including two convolution layers, two batch normalization (BN) layers and two ReLu [21] layers. The convolutional module is shown in the Figure 6. Inspired by DenseNet [22], our model uses feature maps connection mechanism, that is, each convolutional module combines its own input feature maps and the output feature maps together as the input to the next layer, so that feature maps of each layer can be utilized by all the later layers.