# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 1 Introduction  
The efficiency and functionality of computer vision algorithms and various intelligent systems, such as object detection [1], target tracking [2], automated driving [3], and coall-mine-monitoring systems [4], heavily rely on the availability of high-quality images, which has been widely recognized in the field. However, the quality of many images in the real world is not satisfactory due to factors such as the environment, technology, and image acquisition equipment. Particularly in scenarios characterized by inadequate illumination and environmental imbalances, the acquired images manifest diminished contrast, reduced visibility, and elevated ISO noise. In computer vision tasks, low-light conditions often lead to image noise and graininess, making it difficult for traditional computer vision algorithms to extract relevant features and information. However, low-light images can provide valuable information in certain scenes. For example, in surveillance or nighttime imaging, low-light images may capture unique details or events that are not visible under well-lit conditions. Due to the need for noise reduction, contrast enhancement, and other preprocessing steps, processing low-light images typically requires additional computational resources. The processing speed of low-light images may vary depending on thecomplexity of the scene and the algorithm used. Typically, the more complex algorithms used for denoising and enhancing low-light images may require longer processing times. Real-time applications may face challenges in achieving high frame rates when dealing with low-light conditions. High-light images can provide better visibility of objects and details in well-lit environments. In outdoor scenes or environments with strong lighting, high-light images can provide clearer features, making it easier to detect and recognize objects. Compared with low-light images, processing high-light images may require fewer computational resources, as they are typically less affected by noise and require less preprocessing. Due to the potentially lower computational requirements, processing high-light images may result in faster processing speeds compared with low-light images. In addition, low-light images not only affect the visual experience but also convey erroneous information that affects the judgment of humans and multiple intelligent systems. Hence, there has been a huge emphasis on developing techniques to improve low-light image quality. Many advanced approaches have been developed to enhance low-light images. Among them, deep learning methodologies [5; 6; 7; 8; 9] have shown commendable performance in image enhancement owing to the utilization of extensive datasets. However, a notable drawback is their reliance on paired data for training, posing challenges in capturing disturbed and ground truth images of the visual scene simultaneously, particularly in real-world scenarios [10]. While generative adversarial network (GAN)-based approaches address the need for paired data by generating reference images, they still need unpaired normal-light images as a reference and require careful adjustments to the training data to prevent overfitting [11]. Methods based on curve fitting [12; 13] can achieve image enhancement without using paired or unpaired data, but these methods only consider the mapping between image pixels and image exposure, color, and luminance when performing curve fitting. This fails to address the issue of noise in the enhanced image, and the process of image enhancement concurrently amplifies the existing image noise, which seriously affects the image-enhancement effect and visual effect. Consequently, existing low-light image-enhancement methods encounter two principal challenges: (1) dependency on paired or unpaired data for training and (2) without a reference, inherent noise problems that persist in low-light image-enhancement methods. Motivated by the work of [PERSON] et al.