# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 1 Introduction  
Low-light image enhancement is an important branch of image processing, which is mainly applied in fields such as photo surveillance, intelligent driving, and military applications [1]. The main causes of low-illumination images are low light at night and backlighting. Images produced in such environments are generally characterized by low contrast and high noise, which seriously affect the amount of information that can be reflected in the image. In certain circumstances, low-light images can even become distorted, and black can completely obscure the original image information [2; 3; 4]. Therefore, restoring the brightness of low-light images and improving image contrast is a meaningful research topic.  
Various algorithms have been created for processing low-light images. There are three main types of traditional methods for enhancing images at low light: image defogging, the histogram-based method, and the Retinex-based method. These methods are not very effective in processing images, highlighting problems such as color distortion and noise amplification. Therefore, it is of utmost importance to constrain image noise and equalize image color while enhancing image brightness and contrast, which is a hot topic of research in the field of low-light image enhancement (Figure 1).  
The field of image processing has been greatly impacted by deep learning. Research is needed to develop it. It has been observed that certain methods that combine the techniques of deep learning and the Retinex framework for low-light image enhancement have been proven to be effective, such as RetinexNet [5] and R2 RNet [6]. However, existing methods suffer from problems such as amplified noise, color distortion, and loss of image detailsafter enhancement. Therefore, we propose a new method for extracting images from the network using an attention mechanism that is different from deep learning and the Retinex theory. Furthermore, we introduce a color consistency loss function to the other loss functions to further improve the image quality. The proposed method's flowchart can be seen in Figure 2. The main contributions of this paper are as follows:  
1. We designed a new decomposition module with the introduction of residual connections, aiming to reduce the loss of detailed information during the decomposition process and obtain more accurate illumination and reflection maps.
2. An effective method is proposed for low-illumination image enhancement.
3. By introducing an attention mechanism to improve the network structure and adding a color consistency loss function, the illumination and reflection images can be effectively obtained via the processing.
4. The proposed method is significantly better than the current techniques for improving low-light images when it comes to visual perception and evaluation indexes.  
The paper begins with Section 2, which covers the various works related to the Retinex theory. In Section 3, we introduce the proposed model, as well as the loss functions that are utilized. In Section 4, we conduct experimental evaluations, including subjective assessments. The paper concludes in Section 5.