# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 4 Experiments
### Comparison Analysis on the Raw Domain Datasets  
In this part, many experiments were conducted with raw domain dataset SID; firstly, four channels, R-G-G-B, in images from SID are fused into three channels, R-G-B; then, the generated images are viewed as input images of 12 LIE models in Table 2. According to the experimental results of these 12 models, it is demonstrated that within the raw domain of SID, our method maintains state-of-the-art performance regarding both PSNR and SSIM. Specifically, compared to the second-best approach, SNR, our method exhibits enhancements of 0.23 dB and 0.019 in PSNR and SSIM on the SID dataset, respectively. These quantitative results illustrate the effectiveness of our low-light image enhancement technique in the raw domain. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**LOL-v1**} & \multicolumn{2}{c}{**LOL-v2-Real**} & \multicolumn{2}{c}{**LOL-v2-Synthetic**} \\ \cline{2-7}  & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** \\ \hline Zero-DCE [16] & 14.86 & 0.562 & 18.06 & 0.580 & 17.84 & 0.730 \\ DeepUPE [18] & 14.38 & 0.446 & 13.27 & 0.452 & 15.08 & 0.623 \\ EnGAN [40] & 17.48 & 0.650 & 18.23 & 0.617 & 16.57 & 0.734 \\ UFormer [41] & 16.36 & 0.771 & 18.82 & 0.771 & 19.66 & 0.871 \\ MIRNet [17] & 24.14 & 0.830 & 20.02 & 0.820 & 21.94 & 0.876 \\ Sparse [36] & 17.20 & 0.640 & 20.06 & 0.816 & 22.05 & 0.905 \\ Retinexformer [9] & 25.16 & 0.845 & 22.80 & 0.840 & 25.67 & 0.930 \\ RQ [42] & 25.24 & 0.855 & 22.37 & 0.854 & 25.94 & **0.941** \\ SNR [4] & 26.72 & 0.851 & 27.21 & 0.871 & 27.79 & 0.928 \\ Ours & **29.49** & **0.861** & **29.48** & **0.889** & **29.92** & 0.933 \\ \hline \end{tabular}
\end{table} 
Table 1: Quantitative results on the LOL-v1, LOL-v2-real, and LOL-v2-synthetic datasets, where the best results are highlighted in bold, and the second-best results are underlined. The ‘\(\uparrow\)’ symbol indicates that higher values are better. The following are the same. Compared with other six approaches (EDSR, ABF, Retinexformer, SID, RED, and SNR), Figure 7 clearly illustrates that the reconstructed images by sRrsR-Net can retain more image details with the assistance of RGB-iRGB metadata. As shown in the enlarged apple appearing in the input image, i.e., the left-top image in Figure 7, our method better ensures that the contour and the details inside are also well restored. In contrast, the compared methods exhibit severe content distortion and artifacts due to their limited information for image reconstruction. For instance, as shown in the lower left corner of the image with the \"marker pen\", our model restores normal lighting conditions while preserving the color and outline of the pen consistent with the real scene. In contrast, the SNR and Retinexformer exhibit ghosting and blurring artifacts. However, our model has certain limitations in some extremely low-light conditions. In the same figure, when reconstructing the apple in the above image, the \"apple\" generated by our model produces a darker result compared to the SID model. Nevertheless, the contour of the apple in our model is more clearly defined compared to other methods, and the SID model's contour is already blurred. Therefore, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **Methods** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** \\ \hline DeepUPE [18] & 29.13 & 0.792 & SGN [43] & 28.91 & 0.789 \\ SID [23] & 28.88 & 0.787 & ABF [44] & 29.65 & 0.797 \\ EEMEFN [45] & 29.60 & 0.795 & DID [46] & 28.41 & 0.780 \\ Zero-DCE [16] & 26.53 & 0.730 & EDSR [46] & 28.41 & 0.780 \\ FIDE [47] & 29.56 & 0.799 & RED [48] & 28.66 & 0.790 \\ SNR [4] & 29.75 & 0.812 & **Ours** & **29.98** & **0.831** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model comparison results on the raw domain dataset.