# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 1 Introduction  
In people's daily lives and in production processes, it is inevitable to capture images under low-light conditions to obtain and store scene information [1; 2; 3]. However, due to the complexity of light sources, the peculiarity of illumination, and the difference in intensity, various problems occur in the captured images. These problems include uneven brightness distribution, where some areas are too bright and others too dark, random noise may appear in the image, which negatively affects its contrast and color deviation. As a result, these low-quality, low-contrast images can significantly reduce the effectiveness of applications such as night surveillance, vehicle detection, facial recognition, and fault detection [4; 5; 6].  
To improve image quality, brightness and contrast must be enhanced to help people obtain richer image information and meet their daily needs. Image contrast enhancement under low-brightness conditions is an important research area in image processing with significant practical and scientific significance. At present, the field of image enhancement algorithm research has witnessed significant progress, with favorable results achieved through continuous exploration and progress. However, there are still many challenges that require further solutions. This field offers wide development opportunities and application prospects, thus attracting researchers to continue investing in this area.  
In practical applications, traditional image enhancement methods provide some restoration effects on various regions of low-brightness images. However, they still fail to restore the true sense of images with rich texture details. The enhanced results may benatural but not obvious, or they may have clear details but too bright an overall brightness, resulting in brightness distortion.  
In this paper, we propose a low-brightness image enhancement algorithm that addresses the mentioned issues. The algorithm incorporates the brightness transformation function, illumination estimation technique, and the Laplacian pyramid. The goal is to achieve a balanced enhancement of image brightness while avoiding excessive enhancement in localized areas, thereby preserving detailed information within the image. First, a novel brightness transformation function is used to obtain two images. Then, the illumination estimation technique is used to construct a weight matrix. Finally, the enhanced image is obtained by merging the two images using the weight matrix and the Laplacian pyramid algorithm. Our method is effective and has been tested on several datasets. Figure 1 shows the algorithm flowchart for the proposed method. The proposed algorithm makes the following contributions:  
(1) The novel brightness transformation function is used to construct an image enhancement model, and the illumination estimation technique is utilized to create a weight matrix that assigns higher values to pixels with superior brightness;  
(2) The combination of a weight matrix and pyramid fusion achieves excellent performance in obtaining natural and detailed images;  
(3) By setting two parameters, we can build our image enhancement model, thereby demonstrating the simplicity and ease of implementation of our algorithm.