# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 1 Introduction  
With the development of society, intelligent devices have gradually become widely used. At the same time, the application field of video images is more extensive. When taking photos, insufficient light will lead to low contrast and overall low brightness, which will not only affect the subjective visual experience brought by appearances but also lead to the loss of image details. This hinders further image processing, such as face recognition and target detection [1]. However, due to bad weather conditions, poor ambient light, and limited performance of shooting equipment, low-contrast, low-light images are everywhere. Therefore, it is of great significance to study low-light image enhancement [2]. In recent years, scholars have tried to find an optimal algorithm to improve the quality of low illumination images. First, let us think of the gray-level transform algorithm. The gray level transform algorithm adjusts the image grayscale range. For example, histogram equalization adjusts a gray space originally concentrated in the image to the uniform distribution of all gray areas of the image [3], to a certain extent, improving the image brightness but did not consider the relationship between the pixels and making the enhanced images lack the sense of reality [4]. When studying the defogging image algorithm, [PERSON] et al. [5] found that the pixel value distribution of the inverted image of the low-light image is very similar to that of the foggy notion, so the defogging algorithm can be applied to low-light image enhancement. Although the algorithm has a good enhancement effect, the enhanced image will appear with the artifact phenomenon [6]. Methods based on the Retinex theory [7] are relatively popular, and nearly one-third of the methods are designed in combination with Retinex theory. Different subnetworksare used to estimate other components of Retinex theory, which usually have good results. [PERSON] et al. [8] proposed a single-scale Retinex algorithm, which uses the constructed Gaussian surrounding function to filter the three color channels of the image, and the filtered image is the estimated illumination component. After logarithmic processing of the original image and illumination image, the output result is obtained by subtracting [9]. The multi-scale Retinex-enhanced algorithm [10], compared with the single-scale algorithm, increases the number of Gaussian wrapping functions, which can be regarded as a weighted sum of multiple single-scale Retinex of different scales. [PERSON] et al. [11] designed a constraint condition formulated by the prior guidance layer of an end-to-end signal for the low-light enhancement of a single image. [PERSON] et al. [12] proposed a color image enhancement algorithm based on adaptive weighted Retinex and wavelet transform. The low-frequency image is enhanced with adaptive weight Retinex, and the improved threshold is used for high-frequency image denoising. Finally, the inverse wavelet transform is used to reconstruct the luminance component. In recent years, the popularity of deep learning has been very high. Its powerful feature representation and nonlinear mapping ability has also been widely used in the field of image, and has achieved great success [13]. [PERSON] et al. [14] first used the method of deep learning to realize low illumination image enhancement (LLNet). They proposed a method based on a depth self-encoder to recognize signal features from low illumination images. In an image with a high dynamic range, the image is brightened adaptively, without supersaturating the lighter part. Experiments were carried out on real low illumination images, and the effectiveness of the LLNet algorithm is proved. The Retinex theory is a very effective low-light image enhancement theory. In the case of the unknown reflection component, it is an ill-posed problem to solve the illumination component, so we can only calculate using the approximate estimation method. In this paper, an illumination image estimation method based on YCbCr color space is proposed.