# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Quantitative Comparison with Different Methods
#### 4.4.2 Compared Methods on Other Datasets  
This study evaluates various low-light image enhancement methods on five datasets: DICM, LIME, MEF, NPE, and VV, using the Naturalness Image Quality Evaluator (NIQE) metric, where lower values indicate better performance, as shown in Table 3. Traditional methods such as Zero-DCE [50] and RetinexNet [58] achieve reasonable but not top-tier results, with NIQE values of 4.58, 5.82, 4.93, 4.53, and 4.81 for Zero-DCE, and 4.33, 5.75, 4.93, 4.95, and 4.32 for RetinexNet. GAN-based methods like EnlightenGAN [67] and KinD [68] show improved performance, with NIQE values of 4.06, 4.59, 4.70, 3.99, and 4.04 for EnlightenGAN, and 3.95, 4.42, 4.45, 3.92, and 3.72 for KinD. KinD++ [69] achieves slightly higher values of 3.89, 4.90, 4.55, 3.91, and 3.82. SNR-Aware [73] performs poorly, with high NIQE values of 6.12, 5.93, 6.44, 6.44, and 11.50, indicating significant issues in preserving image naturalness. DCC-Net [74] shows better performance with NIQE values of 3.70, 4.42, 4.59, 3.70, and 3.28. GSAD [28] achieves very low NIQE values, particularly on the MEF, NPE, and VV datasets, with values of 3.40, 3.55, and 2.60, respectively. Our proposed method outperforms all existing methods, achieving the lowest NIQE values of 3.12, 4.20, 3.67, 3.63, and 3.02 on the DICM, LIME, MEF, NPE, and VV datasets, respectively. This demonstrates that our method not only enhances image quality but also preserves the natural appearance of the images, making it a superior choice for low-light image enhancement tasks. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & **PSNR (\(\uparrow\))** & **LOLv1** & \multicolumn{4}{c}{**LOLv2-real**} & \multicolumn{4}{c}{**LOLv2-synthetic**} & \multirow{2}{*}{**Param(M)**} \\ \cline{2-2} \cline{6-10}  & **PSNR (\(\uparrow\))** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** \\ \hline LIME [10] & 16.760 & 0.560 & 0.350 & 15.240 & 0.470 & 0.415 & 16.880 & 0.776 & 0.675 & - \\ Zero-DCE [50] & 14.861 & 0.562 & 0.335 & 18.059 & 0.580 & 0.313 & & 0.33 & & 0.33 \\ EnlightenGAN [67] & 17.483 & 0.652 & 0.322 & 18.640 & 0.677 & 0.309 & 16.570 & 0.734 & 0.309 & 8.64 \\ RetinexNet [58] & 16.770 & 0.462 & 0.474 & 18.371 & 0.723 & 0.365 & 17.130 & 0.798 & 0.754 & 0.62 \\ DRBN [44] & 19.860 & 0.834 & 0.155 & 20.130 & 0.830 & 0.147 & 23.220 & 0.927 & 0.147 & 2.21 \\ KimD [68] & 20.870 & 0.799 & 0.199 & 17.544 & 0.669 & 0.375 & 16.259 & 0.591 & 0.435 & 8.03 \\ KinD++ [69] & 21.300 & 0.823 & 0.175 & 19.087 & 0.817 & 0.176 & & & & - & 9.63 \\ MIRNet [70] & 24.140 & 0.842 & 0.131 & 20.357 & 0.782 & 0.317 & 21.940 & 0.846 & 0.317 & 5.90 \\ LTFlow [72] & 25.132 & 0.872 & 0.117 & 26.200 & 0.888 & 0.137 & 24.807 & 0.919 & 0.067 & 37.68 \\ LLFormer [71] & 25.758 & 0.823 & 0.167 & 26.197 & 0.819 & 0.209 & 28.006 & 0.927 & 0.061 & 24.55 \\ SNR-Aware [73] & 26.716 & 0.851 & 0.152 & 27.209 & 0.871 & 0.157 & 27.787 & 0.941 & 0.054 & 39.13 \\ Diffusion-LL [25] & 26.336 & 0.845 & 0.217 & 28.857 & 0.876 & 0.207 & - & - & - & - \\ CSAD [28] & 27.839 & 0.877 & 0.091 & 28.818 & 0.895 & 0.095 & 28.670 & 0.944 & 0.047 & 17.36 \\ PyDiff [27] & 27.09 & 0.93 & 0.10 & 24.01 & 0.876 & 0.23 & 19.60 & 0.878 & 0.22 & 97.89 \\ \hline Ours & 27.923 & 0.882 & 0.105 & 29.049 & 0.889 & **0.095** & 28.741 & 0.937 & 0.042 & 19.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisonof methods on LOLv1, LOLv2-real, and LOLv2-synthetic datasets. PSNR is measured in dB, SSIM is unitless, and LPIPS is unitless (lower is better).