# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
###### Abstract
Their work demonstrated that a variant of a stacked sparse denoising autoencoder trained on synthetic data could effectively enhance low-light and noisy images, thereby highlighting the potential of deep learning in addressing the challenges of low-light imaging. Since then, a plethora of deep learning-based low-light-enhancement methods have been proposed, broadly categorized into supervised and unsupervised approaches. In the realm of supervised learning, [PERSON] et al. [28] introduced Deep Retinex Decomposition (RetinexNet), which leverages deep convolutional neural networks to achieve end-to-end learning of Retinex theory. This method significantly advanced the state of the art by integrating the well-established Retinex model with modern deep learning techniques. Subsequently, [PERSON] et al. [29] proposed KinD, a simple yet effective network that decomposes the enhancement task into three distinct sub-networks: a layer decomposition net, a reflectance restoration net, and an illumination adjustment net. This modular design enables KinD to effectively address the complexities of low-light enhancement by tackling different aspects of the problem separately. More recently, [PERSON] et al. [6] introduced Retinexformer, the first Transformer-based algorithm for low-light image enhancement. By estimating illumination and directly predicting the illumination enhancement map, Retinexformer avoids the numerical instability issues often encountered in traditional methods that rely on estimating the illumination map. This innovative approach leverages the strengths of Transformer architectures, such as their ability to capture long-range dependencies and contextual information, thereby achieving state-of-the-art performance in low-light-enhancement tasks. However, supervised learning methods, despite their impressive performance, face significant limitations. One of the primary challenges is the requirement for annotated or paired training datasets. Currently, the availability of paired low-light images is limited, which restricts the scalability and applicability of these methods. Moreover, supervised low-light-enhancement methods often exhibit insufficient generalization capabilities when confronted with complex and varied scenes. This limitation is particularly evident when these methods are applied to real-world low-light images with diverse lighting conditions, where they may fail to effectively adapt and produce satisfactory results. In contrast, unsupervised models have emerged as a promising alternative, exhibiting stronger generalization capabilities. In 2021, [PERSON] et al. [30] proposed EnlightenGAN, a pioneering method that leverages generative adversarial networks (GANs) for deep-light enhancement without paired supervision. This work demonstrated that GANs could be effectively utilized to enhance low-light images by learning the mapping between low-light and normal-light images without requiring paired training data. Building on this foundation, [PERSON] et al. [31] explored the potential of Contrastive Language-Image Pretraining (CLIP) for pixel-level image enhancement and proposed CLIP-LIT, a novel unsupervised backlight image enhancement method. By leveraging the rich semantic information captured by CLIP, CLIP-LIT achieves improved enhancement results while maintaining strong generalization capabilities. In 2024,Chobola et al. [32] introduced CoLIE, a method that redefines the enhancement process by mapping the 2D coordinates of underexposed images to their illumination components. This innovative approach achieves improved results by effectively capturing the spatial relationships and contextual information within the image, thereby providing a more holistic enhancement strategy. In summary, while supervised deep learning methods have achieved remarkable success in low-light image enhancement, their reliance on paired training data and limited generalization capabilities pose significant challenges.