# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 2 Related Works and Methods  
### Related Works  
**Data-driven approach:** In the realm of low-light image enhancement, early methodologies predominantly centered around the Retinex theory [14; 15; 16; 17; 18], which breaks down an image into its reflectance and illumination components, allowing for the adjustment of brightness in low-light images and the alleviation of artifacts. Recently, the advent of deep learning has significantly impacted image-enhancement tasks, with data serving as a pivotal foundation. In this context, methods for enhancing images through data-driven approaches can be broadly categorized into two groups: convolutional neural network (CNN)-based methods [8; 9; 19] and generative adversarial network (GAN)-based methods [10; 20]. CNN-based approaches predominantly operate from supervised training, where pairs of data are essential for model training. CNN-based enhancement models learn from paired low-light images and their corresponding normal exposure counterparts. The efficacy of CNN-based methods hinges on the quantity and quality of the training data, emphasizing the need for adequate and standardized samples. However, the real-world constraints of economic and time investments in collecting paired data often result in insufficient datasets. The datasets may include false or unrealistic samples, subsequently undermining the generalization capabilities of CNN-based methods. On the other hand, unsupervised GAN-based methods alleviate the necessity for paired training data. These approaches utilize unpaired input data to train a low-light image-enhancement network employing a specific discriminator and loss function. Nevertheless, meticulous screening of the input unpaired data becomes imperative to ensure the effectiveness of GAN-based methods. Figure 1: Content-rich areas in the original input image and noise distribution in the enhanced image. **Curve-estimation-based methods:** Curve estimation-based image-enhancement methods [12; 13; 21; 22; 23] enable zero-reference image enhancement and avoid dependence on paired or unpaired data. General S-curve estimation methods [21; 22; 23; 24] achieve regional exposure adjustment by applying nonlinear curve correction to each region of a given image to obtain attributes such as image exposure, color, hue, and contrast adjustment. However, the traditional S-curve adjustment methods have some drawbacks in maintaining local details and bringing halo effects. [PERSON] and [PERSON] [12] proposed an S-curve global optimization algorithm called detail preservation, which pushes each region as far as possible to its desired region, thus preserving local details and avoiding the halo effect. However, the algorithm in the literature [12] does not apply to all low-light scenarios and is relatively computationally intensive. [PERSON] et al. [25] proposed a color image-enhancement technique based on recursive-filtering and contrast-stretching techniques. This image-enhancement technique was driven by statistical measurements of images and implemented under a logarithmic image-processing model. Although this method is highly suitable for implementation on low-power, low-memory embedded devices, it has not been shown to be effective for images with a single color, such as mine low-light images. [PERSON] et al. [13] proposed a lightweight deep CNN that learns the adjustable parameters of the curves through multiple iterations to achieve higher-order curves that are more robust, more accurate, and less computationally intensive for image enhancement. Although the design of [PERSON] et al. is very skillful, the problem of noise and color distortion caused by image enhancement is still not properly solved in some scene images. The ZLEN proposed in this paper surpasses existing curve-fitting methods in three crucial aspects. First, it introduces a color-map-based noise-estimation technique, enhancing image features through the fusion of noise with low-light images.