# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 4 Experiments
### Computational Performance  
To evaluate computational efficiency and inference speed, this study also compares our model with another eight models from three categories: Transformer, CNN, and U-Net-like. The inference speed is measured using PyTorch's serial scheduler on a GPU, with each  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**LOL-v1**} & \multicolumn{3}{c}{**VE-LOL**} \\ \cline{2-7}  & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** \\ \hline LLNet [49] & 17.96 & 0.713 & 0.360 & 17.57 & 0.739 & 0.402 \\ RRDNet [3] & 11.33 & 0.534 & 0.365 & 14.31 & 0.620 & 0.283 \\ RetinexNet [35] & 16.77 & 0.559 & 0.474 & 14.68 & 0.525 & 0.642 \\ Zero-DCE [16] & 14.86 & 0.589 & 0.335 & 21.12 & 0.771 & 0.248 \\ EnlightenGAN [40] & 17.48 & 0.677 & 0.322 & 20.43 & 0.792 & 0.242 \\ KinD [50] & 20.87 & 0.802 & 0.170 & 18.42 & 0.766 & 0.288 \\ KinD++ [51] & 21.30 & 0.823 & 0.160 & 17.63 & 0.799 & 0.226 \\ RUAS [52] & 18.23 & 0.717 & 0.354 & 14.36 & 0.671 & 0.337 \\ LLFlow [53] & 25.19 & **0.872** & 0.113 & 23.85 & **0.888** & 0.146 \\ SNR [4] & 24.61 & 0.842 & 0.151 & 23.52 & 0.874 & 0.216 \\
**Ours** & **29.49** & 0.861 & **0.104** & **29.05** & 0.884 & **0.119** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative analysis results of the model on the VE-LOL dataset’s generalization test. The ‘\(\downarrow\)’ symbol indicates that the smaller values are better.  
Figure 8: sRrsR-Net’s visualization results on the VE-LOL test set. From top to bottom, the images represent real and synthetic scenarios. From left to right, the input, output, and ground-truth images are depicted.  
model tested 20 times on the respective datasets and with the average values taken. All images underwent the same preprocessing, and the input sizes are the same.  
The results of the running time, shown in Table 4 and Figure 9, indicate that our model sRsRsR-Net outperforms the average and exhibits excellent performance. Among the Transformer-based models, the Retinexformer method reduces running time by processing images using a one-stage retinex-based framework. For the U-Net-like models, the Zero-DCE algorithm uses a specific curve to estimate the task, which helps reduce running time and leads to its best performance.  
Although our method performs well in terms of PSNR, SSIM, and LPIPS, it has some limitations when regarding computational cost. Specifically, our method has a high FLOPs (floating point operations) value. This is due to the integration of the RAWFormer module, which is based on Transformer architectures. When calculating global self-attention, the computational complexity increases quadratically with the size of the input image. For an input image comprising \(N\) pixels, the number of operations required for self-attention can be approximated as \(O(N^{2})\). This quadratic scaling implies that even a slight increase in image resolution can significantly escalate the computational demands.