# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 2 Related Work  
Researchers have conducted extensive research on image enhancement, addressing various problems associated with it and proposing a variety of methods [7]. These methods have many focuses, such as improving visibility in low-light images, controlling brightness inconsistencies in images, and improving results by integrating multiple images. Each method has its own distinct advantages. There are global image enhancement algorithms, such as histogram equalization and gamma correction [8]. Histogram equalization stretches the spatial distribution of image brightness, enhancing the brightness of the majority of pixels and increasing image contrast. The advantage of the global image enhancement algorithm is simple and effective. However,  
Figure 1: Flowchart of the algorithm for the proposed model. the global enhancement methods do not pay attention to the brightness distribution of the image, so it is difficult to ensure the image quality. Moreover, many image enhancement algorithms rely on the Retinex theory [9]. The theory relevant to Retinex is applied to the field of image enhancement, whose purpose is to decompose an image into an illumination map and a reflectance map. The reflectance map can be considered as the final result. The Retinex model can achieve balance in multiple tasks and adapt to different types of images for adaptive enhancement. In contrast, traditional global enhancement methods are limited in their ability to enhance specific features of the image, often resulting in color distortion or loss of detail. Early experiments with the Retinex theory include single-scale Retinex (SSR) [10], multiscale Retinex (MSR) [11], and multiscale Retinex with color restoration (MSRCR) [12]. Using the Retinex algorithms to improve image quality can effectively improve image sharpness and brightness. However, the Retinex algorithms rely on smooth changes in the original light image, and, in real situations, the illumination at the edge of regions with significant brightness differences is usually not smooth. Thus, the application of the Retinex algorithm will lead to the appearance of a halo phenomenon in the resulting image. In addition, there are many classical image enhancement algorithms. [PERSON] et al. [13] proposed a fast and efficient algorithm for low-light video. They treat the inverted low-light image as a blurred image that can be enhanced by dehazing methods. This algorithm has the advantage of fast computation speed, but it produces images with excessive noise. [PERSON] et al. [14] introduced an illumination estimation-based model (LIME) to enhance low-light images. The enhanced images of LIME are visually pleasing, but the robustness of this method is low. [PERSON] et al. [15] introduced a naturalness-preserving enhancement algorithm (NPE) for non-uniform illumination images. The enhanced images of NPE are visually pleasing; however, this method has a relatively high computational complexity. [PERSON] et al. [16] introduced a weighted variation model for simultaneous reflection and illumination estimation (SRIE), but the SRIE method's performance shows that the enhancement effect is weak. Therefore, [PERSON] et al. [17] also proposed a fusion-based enhancement method for weakly illuminated images (MF). The proposed model enhances the derivative amplitudes at brighter points using a weighted variation approach. The experimental results demonstrate the effectiveness of this method in enhancing image brightness and fidelity. However, this method cannot restore texture details. [PERSON] et al. [18] proposed a novel image contrast enhancement based on joint histogram equalization. This algorithm effectively exploits the information between each pixel, but it suffers from a slow computation speed. Recently, deep learning methods have been widely used in computer vision and can also achieve good performance in the underlying image processing. [PERSON] et al. [19] used a depth autoencoder to enhance images with low brightness. [PERSON] et al.