# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 1 Introduction
However, this approach inadvertently introduced noise and resulted in some degree of edge blurring. [PERSON] et al. [22] used guided filtering to estimate the illumination of the image's luminance component and used combined gamma correction [23] to adjust the incident and reflection components. This reserved the image's colors to a certain extent but still resulted in an overall darker image. Second, the atmospheric scattering model is utilized to defog inverted low-light images. This method leverages the similarity between inverted low-light images and hazy images, integrating prior knowledge, such as the dark channel prior [24], to effectively eliminate haze from the image and consequently enhance the quality of the low-light image. [PERSON] et al. [25] introduced an innovative algorithm, grounded in the atmospheric scattering model, to directly apply channel-wise prior dehazing techniques after inverting the low-light images. However, this algorithm introduces excessive noise, making the overall image appear unnatural. [PERSON] et al. [26] used Block Matching and 3D filtering (BM3D) [27] for denoising in order to separate the base layer and enhancement layer of the image, adjusting each layer separately to achieve better results. These methods have achieved certain effects in low light image enhancement, but they lack a physical basis and may introduce excessive noise, resulting in a certain degree of blurring and making the overall image appear unnatural. In recent years, with the surge of interest in deep learning, several deep learning models have emerged that are tailored for low-light image enhancement. Existing deep learning models can be broadly categorized into supervised and unsupervised types. Supervised learning models are designed to automatically discern and learn the intricate mapping relationship between low-light and normal-light images. By deeply analyzing and exploring the inherent characteristics of low-light images, these models are capable of achieving high-quality image enhancement and restoration. [PERSON] et al. [28] proposed the Pyramid Enhancement Network (PE-Net), utilizing a Detail Processing Module (DPM) to enhance image details and validate the algorithm's utility in nighttime object detection tasks in conjunction with YOLOv3. [PERSON] et al. [29] introduced a Differentiable Image Processing (DIP) module to improve the object detection performance under adverse conditions, enhancing images through a weakly supervised approach for object detection. This enabled adaptive image processing under both normal and unfavorable weather conditions, albeit with limited image enhancement effects. [PERSON] et al. [30] introduced a pioneering Gated Differentiable Image Processing (GDIP) mechanism, which facilitated the concurrent execution of multiple DIP operations, thereby enhancing the efficiency and flexibility of image processing tasks. Inspired by the Retinex theory, [PERSON] et al. [31] presented RetinexNet, which achieved remarkable results under low-light conditions but exhibited limitations in processing color information and edge details, resulting in blurred edge areas and distorted edge details during the smooth denoising of the reflectance image. To address color deviation issues, [PERSON] et al. [32] introduced Retinexformer, composed of illumination estimation and image restoration, using an illumination-guided Transformer in order to suppress noise and color distortion. However, this algorithm failed to balance brightness adjustment between bright and dark areas, easily causing overexposure in the originally brighter regions of the image. [PERSON] et al. [33] established the KinD++ network, which not only enhanced the brightness of dark areas but also effectively removed artifacts by separately adjusting illumination and removing degradation in the reflectance map. Unsupervised deep learning models, which do not require labeled data, exhibit strong generalization capabilities. However, due to the lack of direct supervision, their enhancement effects may be less stable.