# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 1 Introduction
Although, the methods based on dehazing model improve the low-light images visually, they are prone to edge effects and tend to produce unrealistic results. In addition, some algorithms transform the RGB image into other color spaces to achieve image enhancement [12; 13], and avoid color distortion whereas, the brightness and contrast still need to be further improved. Recently, deep neural network has got the dramatic improvements in image classification [14], detection [15], tracking [16] and so on. Deep learning is to establish a network model similar to human brain information processing mechanism. It uses efficient learning strategies to extract features step by step in order to fit the complex nonlinear functions. However, at present, low-light image enhancement methods based on deep learning still need research development and improvement. As far as we know, there are few related documents. [PERSON] et al. [17] tried to train the Stacked Sparse Denoising Auto-Encode (SSDA) to identify signal features from synthetic low-light images and corresponding brighten images. Definitely, after appropriate training, the artificial neural network can enhance the low-light images as well. As an outstanding representative of deep learning, the Convolutional Neural Network (CNN) need few training parameters and thereby simple training procedure. In addition, they are inherently robust to various distortions and invariant images such as zoom, rotation and translation because of local receptive field, weight sharing and pooling. In this paper, we propose a method based on the color model transformation and the convolution neural network for low-light sensor image enhancement. At first, the low-light image is converted from RGB to HSI color space. Then the hue component (\(H\)) is kept constant, the saturation component (\(S\)) is enhanced by using the segmentation exponential method, and the intensity component (\(I\)) is improved by the Deep Convolution Neural Network (DCNN). Finally, the output is transformed into the RGB space to obtain the enhanced image. Comparing the experimental results with different methods, in terms of subjective and objective criteria, approve that the proposed method outperforms other state-of-the-art methods. As a conclusion, in following, we refer to the main contributions of this paper. (1) Overcome the shortage of training samples by proposing a simple and time-consuming method for synthesizingthe low-light images based on Retinex model. (2) Enhance the low-light sensor images and avoid the color distortion by applying the segmentation exponential algorithm to enhance the saturation (S), and using specially designed end-to-end DCNN to improve the intensity (\(I\)) which can learn the mapping between the intensity of low-light image and the intensity of bright image after training. (3) Improve the low-light synthetic and real images as well. The paper is organized as follows: In Section 2, we review Retinex model, HSI color space and classical convolutional neural network. In Section 3, the overall framework of our algorithm is explained, where the saturation (\(S\)) component is improved by segmentation exponential method and the intensity (\(I\)) component by DCNN. In Section 4, we show the effectiveness of our method in improving brightness, contrast, and preserving the original image color. Finally, in Section 5 we have conclusion and give the future research work.