# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 2 Related Work  
### Image Enhancement  
Image enhancement is a key task in the field of computer vision, primarily aiming to improve the visual quality of images, making them more suitable for human observation or computer processing [22; 23; 24]. In recent years, deep learning methods have made significant progress in the field of image enhancement, achieving this by learning the mapping relationships from low-quality to high-quality images through deep neural networks. Common visual disturbances include low light, glare, blur, and rain streaks.  
For low-light problems, traditional low-light enhancement methods mainly include histogram equalization [25; 26] and retina model-based methods [27; 28; 29]. Histogram equalization enhances contrast by adjusting the grayscale distribution of an image, while the retina model simulates the visual processing mechanisms of the human retina to improve image quality. However, these methods often have limited effectiveness in handling complex nighttime lighting environments. With the popularity of neural networks, some models enhance images in an end-to-end manner, while others use unsupervised methods to adapt to complex nighttime lighting. Additionally, many models have been designed based on physically interpretable retina theory. For example, [PERSON] et al. [30] used camera response function (CRF) estimation and HDR imaging methods to suppress glare; [PERSON] et al. [31]  
Figure 3: The pipeline for nighttime image enhancement, named LightImg, processes low-light raw images and produces enhanced outputs. Utilizing color restoration enables us to process each image more accurately, achieving performance comparable to that of daytime images.  
performed unsupervised learning through integrated layer decomposition and light effect suppression. Although these methods improve image quality to some extent, they often perform poorly in nighttime scenes where low light and glare occur simultaneously.  
Under low-light conditions, nighttime images may become blurry, especially in adverse weather such as rainy days, where rain streaks and puddle reflections can cause significant interference. To address these issues, some methods attempt to enhance complex rainy scenes [21, 30, 31, 32, 33, 34, 35, 36]. For example, deep learning methods can effectively remove rain streaks and suppress reflections. However, although these methods can improve human visual quality, they do not consider the significant differences between human perception and machine vision, nor the applicability of these methods in other visual tasks.  
Existing methods for processing images under low-light conditions still have some shortcomings. Specifically, they find it difficult to quickly process global illumination while using minimal computational resources, and they may not effectively suppress noise under complex lighting conditions. Furthermore, the applicability of these methods in different visual tasks is also limited.  
To solve these problems, we propose a lightweight image enhancement method. This method not only quickly processes global illumination but also effectively suppresses noise under complex lighting conditions while using minimal computational resources. Through this method, we can achieve efficient and robust image enhancement in practical applications, thereby improving the performance and reliability of nighttime object detection.