# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
###### Abstract  
In recent years, the continuous advancement of deep learning technology and its integration into the domain of low-light image enhancement have led to a steady improvement in enhancement effects. However, this progress has been accompanied by an increase in model complexity, imposing significant constraints on applications that demand high real-time performance. To address this challenge, inspired by the state-of-the-art Zero-DCE approach, we introduce a novel method that transforms the low-light image enhancement task into a curve estimation task tailored to each individual image, utilizing a lightweight shallow neural network. Specifically, we first design a novel curve formula based on Gamma correction, which we call the Gamma-based light-enhancement (GLE) curve. This curve enables outstanding performance in the enhancement task by directly mapping the input low-light image to the enhanced output at the pixel level, thereby eliminating the need for multiple iterative mappings as required in the Zero-DCE algorithm. As a result, our approach significantly improves inference speed. Additionally, we employ a lightweight network architecture to minimize computational complexity and introduce a novel global channel attention (GCA) module to enhance the nonlinear mapping capability of the neural network. The GCA module assigns distinct weights to each channel, allowing the network to focus more on critical features. Consequently, it enhances the effectiveness of low-light image enhancement while incurring a minimal computational cost. Finally, our method is trained using a set of zero-reference loss functions, akin to the Zero-DCE approach, without relying on paired or unpaired data. This ensures the practicality and applicability of our proposed method. The experimental results of both quantitative and qualitative comparisons demonstrate that, despite its lightweight design, the images enhanced using our method not only exhibit perceptual quality, authenticity, and contrast comparable to those of mainstream state-of-the-art (SOTA) methods but in some cases even surpass them. Furthermore, our model demonstrates very fast inference speed, making it suitable for real-time inference in resource-constrained or mobile environments, with broad application prospects. 202515738202516 May 2  
and poor contrast [1; 2; 3; 4]. These adverse characteristics not only severely impair human visual perception and degrade user experience but also severely hinder the performance of critical computer vision tasks, such as image segmentation, object detection, and face recognition [5; 6; 7; 8]. Consequently, the enhancement of images captured under low-light conditions constitutes a vital research priority. At present, the existing low-light image enhancement techniques can be broadly classified into two categories: traditional methods and deep learning-based approaches. The traditional methods for low-light image enhancement primarily encompass histogram equalization [9; 10; 11], Gamma correction [12; 13; 14], and Retinex theory [15; 16; 17]. These techniques have been widely utilized due to their simplicity and intuitive principles, yet they suffer from various limitations when applied to complex low-light scenarios. Histogram equalization is a well-established technique that enhances image contrast by redistributing the pixel value distribution. However, it often leads to over-enhancement, noise amplification, and color distortion [18], particularly in images with narrow or skewed histograms. These artifacts can significantly degrade the visual quality of the enhanced images, making histogram equalization less suitable for low-light-enhancement tasks that require fine detail preservation and natural appearance. Retinex theory-based methods decompose images into reflection and illumination components [19], achieving enhancement by adjusting the illumination component.