# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 4 Results
### Quantitative Comparisons  
To further evaluate the effectiveness and advancement of our proposed model, we conducted comprehensive metric assessments on six different datasets, including LOLv1, LOLv2-Real, LOLv2-Synthetic, LSRW-huawei, DICM, and MEF. In all the reference metric evaluations, we highlight the best results in bold and underline, while the second- and third-best results are marked in bold. We measured reference metrics PSNR, SSIM, LPIPS, and the non-reference metric NIQE, as shown in Tables 3-5 below. Tables 3 and 4 display the comparative results of the reference metrics. It can be observed that our method demonstrates significant advantages regarding the PSNR, SSIM, and LPIPS metrics. In the PSNR metric comparisons, our method is only slightly inferior to R2 RNet on the LOLv1 dataset while achieving the highest metrics on the other three datasets. In terms of the SSIM metric comparisons, our method achieves commendable results, attaining the best performance on the LOLv1 and LSRW-huawei datasets, and securing a position within the top three on the LOLv2-Real dataset. Regarding the LPIPS metric comparison, our approach achieves the best results on both the LOLv2-Synthetic and LSRW-huawei datasets, and also ranks third on the remaining two datasets. Additionally, since the numerical results in the top  
Figure 10: Visual comparisons of various methods on MEF dataset. (**a**) input image. (**b**) NIQE = 2.501; (**c**) NIQE = 2.141; (**d**) NIQE = 4.307; (**e**) NIQE = 5.859; (**f**) NIQE = 2.350; (**g**) NIQE = 2.179; (**h**) NIQE = 2.597; (**i**) NIQE = 2.560; (**j**) NIQE = 2.347. Figure 9: Visual comparisons of various methods on DICM dataset. (**a**) input image. (**b**) NIQE = 3.142; (**c**) NIQE = 2.040; (**d**) NIQE = 4.894; (**e**) NIQE = 4.496; (**f**) NIQE = 2.289; (**g**) NIQE = 2.428; (**h**) NIQE = 2.691; (**i**) NIQE = 2.126; (**j**) NIQE = 2.064.  
performing entries are closely clustered, we introduce a standard deviation column in Table 4 to further demonstrate the stability of the enhancement performance. Overall, judging from the standard deviation indicator in the experimental data, the standard deviation value of the proposed algorithm ranks in the upper-middle level among all the algorithms, showing a certain degree of stability, which basically meets our original design intention of achieving enhancement effects comparable to mainstream algorithms while ensuring optimal computational efficiency. The above results with a reference metric evaluation indicate that our proposed method has a better enhancement effect, and the enhanced image is close to the real image under normal light. Throughout these comparisons, we observed that R2 RNet achieved relatively good results across multiple metrics and datasets, possibly because it employs supervised learning with a more complex model, whereas our model is unsupervised with a simpler structure. Furthermore, Tables 3 and 4 demonstrate that, across all the datasets, our method consistently outperforms Zero-DCE and Zero-DCE++ in the PSNR, SSIM, and LPIPS metrics, validating the superiority of our curve estimation method, which generates enhanced images more closely resembling the real normal-light reference images. In Table 5, we present a comprehensive comparison of various methods across six datasets using the no-reference indicator NIQE. The results demonstrate that our proposed model achieves good performance in various datasets. Specifically, our model attains