# A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model
## 4 Experimental Results
### Qualitative Comparison  
To visually analyze the denoising effects of the MPMTDIP model, we first applied various denoising methods to the starfish image from the Set12 dataset, which was subjected to Gaussian noise with a noise level of 50. The overall denoised image and the visual effects of specific localized areas are shown in Figure 6. From the enlarged sub-images, it is evident that for areas with color uniformity, supervised deep-learning-based denoising models like FFDNet, DRUNet, and SwinIR tend to produce overly smoothed results. The performance of the unsupervised DIP algorithm is also suboptimal. The MT model, which uses training models with Gaussian noise levels of 15, includes a masking module to enhance network generalization. However, as it remains a supervised denoising model, its performance on noise images with a level of 50 is poor. In contrast, the MPMTDIP model, which integrates both supervised and unsupervised learning strategies, shows significant advantages, with restoration results that are closest to the original image and exhibit the best noise reduction and detail preservation capabilities. To further analyze the visual effects of the MPFDIP denoising model on images with Gaussian-Poisson mixed noise, various denoising methods were applied to the test004 image from the BSD68 dataset, labeled with mixed noise intensity number 2. The enlarged sub-images shown in Figure 7 reveal that in the duckbill portion of the test004 image, residual noise remains noticeable in the outputs of DnCNN, FFDNet, DRUNet, SwinIR, and Restormer models. This is attributed to the fact that supervised denoising models often experience a decline in performance when dealing with images with noise distributions different from those encountered during training. Additionally, the unsupervised DIP denoising model exhibits poorer results due to severe damage in the target images. Although the MT model, based on a masking strategy, incorporates a masking module to enhance network generalization, it is inherently a supervised model; thus, its denoising performance on Gaussian-Poisson mixed noise images is still significantly impacted. In contrast, compared to other denoising methods, MPFDIP demonstrates superior noise reduction capabilities for mixed noise, better preserves image details, and produces images with a closer resemblance to the original image. Its ability to remove more noise from images surpasses that of supervised deep models. Additionally, to validate the visual effects of the MPFDIP denoising model under real noise conditions, various denoising methods were applied to noisy images from the low-dose CT dataset. The results are illustrated in Figure 8. Observations of the detailed sections of the denoised images reveal that the results from DnCNN and FFDNet exhibit lower clarity. The outcomes from DRUNet and SwinIR are relatively smoother, while the results from MT and DIP appear more blurred. In contrast, the outcomes from the MPFDIP model closely resemble those of normal-dose CT images, effectively reducing the interference caused by noise and providing a higher image clarity. This once again underscores its strong generalization capabilities.