## 1 Introduction  
With the rapid development of computer vision technology, images are widely used in the fields of surveillance equipment, satellite remote sensing, and medical imaging. However, in the process of image acquisition, the imaging device cannot obtain a sufficient number of photons due to the influence of illumination factors, which leads to underexposure of the image. In underexposed images, the contrast of the scene is low, and darker areas occupy most of the image, and the visual effect of the image is poor. If the visibility of the image is improved only by extending the exposure time of the device, motion blur may be caused by the movement of objects in a short period of time. As shown in Figure 1, low-light images not only have the problems of uneven lighting and low definition, but they also have the most difficulty to solve the problem of high noise. These problemsseriously affect the visual effect of human observation and greatly limit the normal function of other computer vision algorithms and vision devices. Therefore, the enhancement of low-light images is an essential task. A great deal of research has been conducted in low-light image enhancement, with researchers using histogram-based methods and various methods based on Retinex. Histogram-based methods, such as [5], attempt to achieve contrast enhancement by reassigning intervals with higher frequency of the original image gray levels to global or local intervals. However, such methods can lead to inadequate or excessive enhancement. While algorithms based on histogram equalization have been proposed, low-light image enhancement algorithms based on Retinex theory and low-light image enhancement algorithms based on convolutional neural networks have also developed rapidly. The Retinex theory proposed by [PERSON] et al. [6] suggests that the human visual system has color constancy, which means that when the illumination on the surface of an object changes, the human eye's perception of the color of the object is not affected. Most of the algorithms based on Retinex theory first estimate the illumination component, then remove the illumination component, and finally keep the reflection component and use it as the enhancement result of the image. To mine the structural information in low-light images, the SSR (Single-Scale Retinex) algorithm [7] uses a Gaussian low-pass filter to estimate the light component, but the color of the object image is prone to distortion, and there is a halo effect near the edges of the object. To solve the problems of SSR, the MSR (Multi-Scale Retinex) [8] algorithm uses a color balance factor to reduce the loss of color information. Although the Retinex-based algorithm is effective in enhancing the contrast and reducing the loss of detail information in dark areas, there is a shortcoming: when the illumination and reflection components cannot be completely separated, the information of the reflection component will be captured by the illumination component. However, most Retinex-based methods only use the reflectance component to enhance the image, which leads to less than optimal enhancement results. A characteristic of low-light images is the variety of noise or speckles in the image, and being able to remove noise and speckles adaptively is the biggest difficulty in image enhancement. Although noise can be dealt with using image noise reduction algorithms, noise reduction itself is a smoothing process that causes severe loss of details.The algorithm of JED (Joint Enhancement and Denoising) [9] introduces denoising in enhancement by using a weight matrix to suppress noise during the decomposition of the illumination and reflection components, and smoothing the noise in the spatial localization of the feature information. Researchers have also conducted extensive studies on how to remove speckles from images without causing loss of detail information [10,11]. Figure 1: Comparison between state-of-the-art methods and ours. RetinexNet (2018) [1], KinD (2019) [2], EnGAN (2020) [3], Zero-DCE++ (2021) [4].