# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 1 Introduction
Drawing upon the principles of the Retinex theory, [PERSON] et al. [34] devised a novel Generative Adversarial Network (GAN) approach that did not necessitate the use of paired datasets. This method achieves remarkable low-light image enhancement through the judicious utilization of controlled discriminator architectures, self-regularized perceptual fusion techniques, and sophisticated self-attention mechanisms. Yet, due to the neglect of physical principles, artifact phenomena can easily arise. Subsequently, [PERSON] et al. [35] presented the RetinexGAN network, a sophisticated framework that is meticulously divided into two distinct components for image decomposition and enhancement. However, it is noteworthy that an overemphasis on distribution during the enhancement process may inadvertently result in unnatural-looking enhanced images. [PERSON] et al. [36] proposed an efficient unsupervised generative adversarial network (EnlightGAN), which utilizes an unsupervised generative adversarial network combined with an attention-guided U-Net generator and a global-local discriminator to enhance low-light images, demonstrating good domain adaptability. [PERSON] et al. [37] introduced an innovative unsupervised backlit image enhancement technique, CLIP-LIT. This method leverages the robust capabilities of the CLIP model and achieves the effective enhancement of backlit images through a prompt learning framework and iterative fine-tuning strategies. However, it is worth noting that this method relies on a pre-trained CLIP model and may fail in some extreme cases, such as when information is missing in overexposed or underexposedareas. [PERSON] et al. [38] pioneered the utilization of the controllable fitting capability of neural representations, proposing an implicit neural representation method named NeRCo for low-light image enhancement, which demonstrates exceptional performance in restoring authentic tones and contrast. [PERSON] et al. [39] presented an algorithm that reconstructs images in the HSV space using implicit neural functions and embedded guided filtering, effectively improving image quality and scene adaptability, although overexposure can still occur in highlight areas. [PERSON] et al. [40] converted low-light image enhancement into a deep curve estimation problem, proposing a zero-reference deep curve estimation (Zero-DCE) network. By setting a series of reference-free loss functions, the network achieves end-to-end training without any reference images. Although brightness is significantly improved, contrast remains low, and processing directly on the RGB channels of the image can easily lead to color distortion. [PERSON] et al. [41] improved upon this by proposing Zero-DCE++, which uses downsampling to input images and learn mapping parameters, followed by upsampling and final image processing with the learned parameters to enhance low-light images. This results in good performances in color preservation and overall smoothness, but may cause overexposure in originally well-exposed areas. Compared with traditional algorithms, deep learning algorithms have more stringent data requirements, and low-light images typically suffer from poor quality, with minimal differences between targets and backgrounds. This leads to the ineffective enhancement of dark areas by deep learning algorithms and insufficient robustness. To address the challenges, we introduce the zero-reference triple-curve enhancement algorithm (Zero-TCE), a novel approach specifically designed to enhance low-light images. It possesses the capability to handle a diverse array of lighting conditions, encompassing backlight scenarios, uneven illumination, and insufficient lighting. By framing the task of low-light image enhancement as a unique curve estimation problem tailored to each individual image, Zero-TCE accepts a low-light image as an input and produces three high-order curves as the output, thereby facilitating significant improvements in image quality.