# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 5 Conclusions  
In this work, we propose a novel lightweight low-light image enhancement method and validate its performance through extensive experiments. The proposed method retains the zero-reference learning strategy of the Zero-DCE method, eliminating the need for paired or unpaired datasets during training, thus making it highly versatile across various application scenarios. The key differentiator between our approach and the Zero-DCE method lies in the significant advancements achieved in both inference speed and image quality. These improvements are primarily attributed to our redesigned GLE curve and a lightweight network architecture that incorporates a GCA mechanism. Specifically, we design a new curve based on Gamma correction principles that requires only a single mapping to complete low-light image enhancement, substantially improving the processing efficiency compared to the Zero-DCE method, which requires eight iterations of the LE curve application to accomplish low-light image enhancement. Additionally, we introduce a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **RetinexNet** & **R2 RNet** & **RUAS** & **Zero-DCE++** & **CLIP-LIT** & **GCP** & **CoLIE** & **Ours** \\ \hline Time (ms) & 95.1 & 1345.0 & 3.5 & **1.0** & **1.9** & 49.0 & 1243.0 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of average computation time per image (ms) on the LOLv2-Real dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**NIQE \(\downarrow\)**} \\ \cline{2-7}
**Method** & **LOLv1** & **LOLv2-Real** & **LOLv2-Synthetic** & **LSRW-Huawei** & **DICM** & **MEF** \\ \hline RetinexNet & 8.734 & 9.254 & 5.346 & 4.063 & 4.438 & 4.344 \\ Zero-DCE & 7.755 & 8.079 & **4.485** & **3.511** & **3.807** & **3.435** \\ R2 RNet & **4.721** & **4.984** & 5.214 & 5.121 & 5.203 & 4.886 \\ RUAS & **6.291** & **6.501** & 6.530 & 5.699 & 6.762 & 5.369 \\ Zero-DCE++ & 7.871 & 8.106 & 4.522 & 3.757 & 3.947 & 3.537 \\ CLIP-LIT & 8.102 & 8.213 & 4.553 & 3.902 & 3.938 & **3.461** \\ GCP & 9.211 & 9.156 & 4.665 & 4.020 & **3.880** & 3.586 \\ CoLIE & 8.204 & 8.283 & **4.503** & **3.494** & **3.761** & **3.410** \\ Ours & **7.600** & **7.860** & **4.455** & **3.716** & 3.882 & 3.465 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the non-reference metric (NIQE) across the LOLv1, LOLv2-Real, LOLv2-Synthetic, LSRW-huawei, DICM, and MEF datasets.  
GCA mechanism, which enables the model to focus more effectively on key features, thereby significantly enhancing performance in low-light-enhancement tasks. Although our method demonstrates encouraging outcomes, its image quality performance still lags behind certain advanced supervised and unsupervised techniques. Consequently, subsequent research will investigate strategies for boosting generated image quality without compromising the model's lightweight nature, providing more efficient and robust solutions for practical application scenarios.  
[PERSON] and [PERSON] contributed to the conception of the study, [PERSON] drafted the main manuscript text, [PERSON] significantly contributed to the manuscript revision, and [PERSON], [PERSON] and [PERSON] made significant contributions to the analysis and performed the associated experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 62162043.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to express their sincere gratitude to the contributors of the source code for this study, which helped us to conduct our research and improve the quality and reproducibility of our work.  
The authors declare no conflicts of interest.