# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 4 Results
### Analysis of the Impact of Different Modules on FRET  
To validate the effectiveness of the full-scale skip connections in FRET and to assess whether the order of applying GFRN and GCEN affects the network's performance, we conducted a series of experiments using the same PSR simulation dataset and experimental settings, with 55 training epochs. The experimental results are presented in Table 3. In this experiment, \"Baseline\" refers to the UFormer model, \"A\" indicates the introduction of full-scale skip connections, \"B\" refers to the addition of the GFRN module, and \"C\" denotes the inclusion of the GCEN module. Since the RE-Win module contains two sets of SW-MSA and FFN, \"B+B\" signifies that both FFNs in the two networks are constructed with GFRN, while \"C+C\" means both FFNs in the two networks are constructed with GCEN. \"C+B\" represents the configuration where the FFNs in the two networks first use GCEN and then GFRN, and \"B+C\" indicates the configuration where the FFNs in the two networks first use GFRN followed by GCEN. The experimental results show that after incorporating full-scale skip connections (A), the MAE decreases from 3.3185 to 3.2900, a reduction of 0.0285, while SSIM remains at 0.9957, and PSNR increases from 61.8474 to 61.9904, an improvement of 0.143. This indicates that full-scale skip connections help to enhance the fusion of information across different scales, improving the model's ability to handle complex data, especially reflected in the significant reduction in MAE. For Baseline+A, after the introduction of the GFRN module, Baseline+A+B+B results in a MAE of 2.9918, a reduction of 0.2982. SSIM improves from 0.9957 to 0.9966, with an increase of 0.0009, and PSNR rises from 61.8474 to 62.7690, an increase of 0.9216. This demonstrates that the GFRN module significantly improves MAE by reducing redundant computations and optimizing feature information, while also enhancing image quality, especially in PSNR and SSIM metrics, validating its crucial role in optimizing feature information and improving image quality. Compared to Baseline+A+C+C (using the GCEN module), Baseline+A+B+B shows superior performance. Baseline+A+C+C achieves a MAE of 2.9525, a reduction of 0.3375, SSIM increases from 0.9957 to 0.9966, an increase of 0.0009, and PSNR increases from 61.8474 to 62.7622, an improvement of 0.9148. Although the GCEN module excels in enhancing local feature extraction, the GFRN module shows stronger advantages in optimizing global features. Therefore, the overall performance of Baseline+A+B+B exceeds that of Baseline+A+C+C. Using a combination of GCEN and GFRN modules (Baseline+A+C+B) resulted in some performance improvements. Compared to Baseline+A+B+B, the MAE drops to 2.9976, a reduction of 0.0058. SSIM increases from 0.9966 to 0.9967, with an increase of 0.0001, and PSNR increases from 62.7690 to 62.8227, an improvement of 0.0537. This combination enhances local feature extraction through GCEN and further optimizes feature information through GFRN, reducing redundant computations and achieving a balanced performance improvement. Although Baseline+A+C+B performs well, Baseline+A+B+C demonstrates even better performance. The MAE in Baseline+A+B+C decreases to 2.8575, 0.1401 lower than Baseline+A+C+B's 2.9976. SSIM is 0.9968, slightly higher than 0.9967 in Baseline+A+C+B, with \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluating Indicator** & **Baseline** & **Baseline+A** & **Baseline+A+B+B** & **Baseline+A+C+C** & **Baseline+A+C+B** & **Baseline+A+B+C** \\ \hline MAE & 3.3185 & 3.2900 & 2.9918 & 2.9525 & 2.9976 & 2.8575 \\ SSIM & 0.9957 & 0.9957 & 0.9966 & 0.9966 & 0.9967 & 0.9968 \\ PSNR & 61.8474 & 61.9904 & 62.7690 & 62.7622 & 62.8227 & 63.0946 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation results for the ablation experiments. an increase of 0.0001. PSNR increases to 63.0946, 0.2720 higher than Baseline+A+C+B's 62.8227. This difference arises from the order and complementarity of the modules.