## 2 Related Work  
The weather, light, and other environmental factors frequently have an impact on the image quality in real settings, which causes some image information to vanish in the dark and present low light. Traditional enhancement methods and deep-learning-based enhancement methods make up the two primary categories of low-light picture improvement research. The histogram equalization [1], gamma correction [2], and Retinex theory [3]-based conventional low-light image enhancing methods are among the more reliable ones. Among  
Figure 1: Comparison between low-light and normal images. (**a**,**b**,**d**) is a low-light image, where (**d**) contains local strong lighting. (**c**) is the normal-illuminated image. them, histogram equalization stretches the grayscale histogram of the image from a more concentrated grayscale interval to the entire grayscale range, expanding the range of grayscale values in the image and improving the image contrast and part of the detail effect, but it is prone to chromatic aberration and the grayscale merging loses the detail information. Gamma correction adjusts the parameters to change the magnitude of the enhancement of the image luminance by a non-linear function, which also loses the detail information and generates a large amount of noise. The theoretical basis of the Retinex model is the three-color theory and color constancy, which removes or reduces the influence of the incident image by the same method to preserve the image of the essential reflective properties of the object as much as possible. Retinex theory has been the subject of ongoing research, leading to the development of algorithms like SSR (single-scale Retinex) [4], MSR (multi-scale Retinex) [5], and MSRCR (multi-scale Retinex with color recovery) [6]. Traditional algorithms have the advantages of a fast processing speed and easy deployment, but they lack references to real lighting conditions and suffer from problems such as noise being retained or amplified, artifacts, and color deviations. From both supervised and unsupervised learning viewpoints, low-light image enhancement methods based on deep learning can be distinguished. [PERSON] et al. proposed LLNet (Low-Light Net) [7] for supervised learning. This network adopts the traditional self-encoder and decoder structures and improves image contrast and denoising by stacking sparse denoising self-encoders, but in order to easily obtain the paired dataset, artificially synthesized low light and noise images are used during the training process, which leads to poor generalization ability and enhancement effects in real scenes. RetinexNet [8], a convolutional neural network approach that [PERSON] et al. proposed, is based on the Retinex theory. The input image is segmented into reflection and illumination maps by a DecomNet subnetwork and the Enhance-Net subnetwork is utilized to adjust and estimate the illumination maps in order to obtain the image after boosting the contrast. Limited by the ideal state of Retinex theory, this algorithm suffers from serious color deviation problems when enhancing extremely dark light images and multi-color light night images. To correct exposure and underexposure from coarse to fine, [PERSON] et al. presented LMSPEC (Learning Multi-Scale Photo Exposure Correction) [9], which formulated the exposure correction problem as two subproblems--color enhancement and detail enhancement--and used a DNN (deep neural network) model, which was trained in an end-to-end manner to correct global color information first and then improve image details; however, the network design was too complex and the network was not able to constrain the color information of a region of an image when the region was completely saturated. In the field of unsupervised learning, EnlightenGAN [10] is a low-light image improvement technique proposed by [PERSON] et al., based on a generative adversarial network (GAN). It includes a U-Net with an attention mechanism as a generator and a pair of \"global-local\" discriminators [11].