# A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model
## 4 Experimental Results
### Quantitative Results  
Initially, denoising effects were compared on the Set12 dataset for grayscale synthetic Gaussian noise images at noise levels of 15, 25, and 50, as depicted in Table 7. The proposed MPFDIP method exhibits superior performance in both PSNR and SSIM compared to other methods. Importantly, the MPFDIP model significantly outperforms the MT denoising model, which also employs a masking strategy, in both PSNR and SSIM metrics. This \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combination No.** & **FFDNet** & **DRUNet** & **SwinIR** & **Restormer** & **PSNR** \\ \hline
1 & ✓ & ✓ & & & 30.85 \\
2 & ✓ & & ✓ & & 30.84 \\
3 & ✓ & & ✓ & ✓ & 30.83 \\
4 & & ✓ & ✓ & & 31.05 \\
5 & & ✓ & & ✓ & 31.06 \\
6 & & & ✓ & ✓ & 31.07 \\
7 & ✓ & ✓ & ✓ & & 30.95 \\
8 & ✓ & ✓ & & ✓ & 30.99 \\
9 & ✓ & & ✓ & ✓ & 31.01 \\
10 & & ✓ & ✓ & ✓ & **31.09** \\
11 & ✓ & ✓ & ✓ & ✓ & 31.03 \\ \hline Baseline & 30.46 & 30.94 & 31.02 & 31.04 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The impact of different combinations of denoising methods on model performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration Steps** & **With the Pre-Trained Model** & **Without the Pre-Trained Model** \\ \hline
0 & 20.45 & 6.79 \\
200 & 29.38 & 21.36 \\
400 & 30.81 & 26.96 \\
600 & 31.03 & 27.98 \\
800 & 31.14 & 28.59 \\
1000 & 31.19 & 29.08 \\
1200 & 31.23 & 29.46 \\
1400 & 31.25 & 29.75 \\
1600 & 31.20 & 29.98 \\
1800 & 31.18 & 30.16 \\
2000 & 31.17 & 30.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PSNR values for different iteration steps with and without the pre-trained model. underscores the crucial role of the unsupervised training aspect of the DIP model in enhancing the denoising performance. Secondly, to evaluate the denoising effect of the MPFDIP method on simulated mixed noise images, a denoising comparison experiment was conducted on the BSD68 dataset with Gaussian-Poisson mixed noise at three different noise level intensities. These tests are represented by identifiers 1, 2, and 3, as shown in Table 8. Identifier 1 corresponds to adding Gaussian noise with a standard deviation of 15 and Poisson noise with intensity 1, identifier 2 corresponds to Gaussian noise with a standard deviation of 25 and Poisson noise with intensity 2, and identifier 3 corresponds to Gaussian noise with a standard deviation of 50 and Poisson noise with intensity 3. The MPFDIP denoising model demonstrates superior PSNR and SSIM values compared to other algorithms when dealing with various levels of Gaussian-Poisson mixed noise. The denoising effect is significantly improved compared to the preprocessing algorithms used, and it is also better than the original DIP denoising model. This indicates that masked pre-training can enhance the generalization capability of the denoising model, making it more robust. Additionally, to assess the denoising performance of the MPFDIP on real noisy images, tests were conducted on the Nam, PolyU, and LDCT real noisy datasets. The results are shown in Table 9. The MPFDIP denoising model leads other comparative algorithms in terms of PSNR values. Although the SSIM values on the Nam and PolyU real noise image datasets are slightly lower than those achieved by the BM3D method, the SSIM values for denoising low-dose CT images are higher than BM3D. Overall, MPFDIP still leads in terms of the average SSIM value compared to other comparative algorithms.