# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 1 Introduction  
With the rapid development of modern computer and multimedia information technology, digital image processing systems have been widely used in industrial production [1], scientific research [2], intelligent transportation [3], and other fields. However, the process of image acquisition is complex and affected by many factors, resulting in various image defects. Insufficient lighting conditions, such as indoor, cloudy, or night conditions, often lead to a serious decline in image quality [4]. Researchers have pursued various improvements from both the hardware and software perspectives. The former is the development of image acquisition equipment [5; 6], such as using professional low light level cameras (e.g., Sony, SiOnyx, and Photonis), but they are expensive and not suitable for daily life. The latter is to improve the collected image through digital image processing. The main research direction of this paper was software-based low-light image enhancement. Enhancing the visual quality of images captured under weak light conditions is a difficult problem. These images usually appear with low contrast, low resolution, high noise, and over-exposure, due to insufficient light and gain, which affect our visual effects seriously. Consequently, low-light image enhancement technology, which aims to improve the overall brightness, local contrast, and detail information of raw images, has been widely studied [7; 8; 9; 10]. Directly increasing the image brightness is the most intuitive and simplest method. Typical algorithms use histogram equalization (HE) and grayscale transformation, which enhancepixel values through mathematical functions. HE and its derivative algorithms [11; 12; 13; 14; 15] are widely used due to their simplicity and effectiveness. Grayscale transformation uses a mathematical function to change grayscale values, and gamma correction (GC) is one of the most widely used methods [16; 17; 18]. These methods enhance the entire image to the same degree regardless of whether the area is dark or not, which easily causes overexposure. Retinex theory [19; 20; 21; 22; 23; 24; 25] assumes that an image can be decomposed into an illumination component and a reflection component, and the latter is used as the final enhanced image. Single-scale retinex (SSR) [20], multi-scale retinex (MSR) [21], and multi-scale retinex with color restoration (MSRCR) [22] are typical algorithms based on retinex theory. However, these methods enhance the image by eliminating illumination, resulting in loss of information. Image fusion-based enhancement algorithms have attracted the attention of scholars because they do not require any physical model but only fuse useful information from multiple images [26; 27; 28; 29]. [PERSON] et al. [27] introduced a fusion method based on a variety of mature image proposing technologies to improve image quality (MF). Unlike the classical retinex method, this method improves shimmering images by maintaining and enhancing image illumination. However, for the enhancement of image brightness, an inverse tangent function is still used, and the enhancement degree of global brightness is the same, leading to a certain degree of overexposure. [PERSON] et al. [28] proposed a fractional-order mask and the fusion framework model (FFM) for low-light image enhancement. However, the model produces an overall darker and less visually appealing result. Recently, deep learning-based methods have been proposed, which extract numerous image features from datasets and hence can effectively improve image quality [30; 31; 32; 33]. [PERSON] et al. [30] proposed a trainable hybrid network that consists of two distinct streams to obtain a clear image. [PERSON] et al. [32] present a dual autoencoder network model to enhance low-light images and reduce noise by combining the stacked and convolutional autoencoders. [PERSON] et al. [33] designed a so-called Retinex-Net (Ret-Net) network, which includes a decom-net for decomposition and an enhancement-net for illumination adjustment.