# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
###### Abstract
In contrast, unsupervised methods, such as EnlightenGAN, CLIP-LIT, and CoLIE, have demonstrated the potential to overcome these limitations by leveraging advanced techniques, such as GANs and CLIP. These developments highlight the ongoing evolution of low-light image enhancement, driven by the continuous integration of deep learning techniques and innovative algorithmic designs. As mentioned above, it is evident that traditional low-light-enhancement methods often necessitate complex manual prior knowledge or intricate iterative algorithms to achieve satisfactory enhancement results, leading to high computational complexity. On the other hand, while the existing supervised and unsupervised deep learning-based low-light-enhancement models have been continuously improving in terms of enhancement effects, these methods still have certain limitations, particularly regarding inference speed. Most deep learning-based low-light-enhancement methods rely on complex network architectures, resulting in large model parameters and high computational costs, making them challenging to run in real time on resource-constrained devices. In recent years, [PERSON] et al. [33] proposed the Zero-Reference Deep Curve Estimation (Zero-DCE) method for low-light image enhancement, which demonstrates unique advantages with its lightweight design and zero-reference training strategy. This method directly adjusts the original image by learning pixel-level curve mapping functions, circumventing the complex feature extraction process and effectively reducing computational complexity. Zero-DCE does not depend on paired datasets for training, instead, it is guided by zero-reference loss functions such as spatial consistency loss, exposure control loss, and color constancy loss, ensuring the naturalness of the enhancement results. Subsequently, to further improve computational speed and reduce the model size, [PERSON] et al. [34] introduced an accelerated and lightweight version of Zero-DCE, called Zero-DCE++. However, despite the partial improvements made by Zero-DCE++, its computational speed is still unsatisfactory, indicating the need for further research and development to enhance the efficiency and practicality of low-light image enhancement methods for real-time applications on resource-limited devices. As is known, the Zero-DCE and Zero-DCE++ methods design a light-enhancement curve (LE curve), a quadratic function that necessitates eight iterations to achieve satisfactory enhancement results. This iterative process, while effective, presents an opportunity for further improvement in terms of computational efficiency. Drawing from the insights of Zero-DCE, we recognize that the low-light image enhancement task can be reformulated as the estimation of a specific curve, enabling the design of a dedicated curve tailored for low-light image enhancement. Consequently, in this work, our objective is to devise a powerful curve that can directly accomplish the enhancement task--specifically the pixel brightness mapping task--excellently without the need for iterative processing. To this end, we conducted an in-depth analysis of the properties of the LE curve. After continuous efforts, we ultimately designed a completely new curve based on Gamma correction theory, termed the Gamma-based light-enhancement (GLE) curve. Furthermore, to enhance both computational speed and enhancement effects, we redesigned the network architecture and introduced a novel global channel attention (GCA) mechanism for channel reconstruction. This attention mechanism builds upon the SE [35] attention mechanism, simplifying the complex squeeze-and-excitation process and retaining the channel weight allocation strategy. By incorporating the GCA module, our model focuses more on important regions, thereby enhancing its generalization ability and overall enhancement performance. In summary, our contributions are as follows:  
1.