# A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model
## 1 Introduction
This reliance poses practical challenges and escalates the expenses linked with data acquisition. [PERSON] et al. developed Neighbor2 Neighbor [27], which leverages noise image subsampling techniques to generate pairs of noisy images, enhancing the performance and reducing the influence of noise distribution on the denoising outcomes. However, the effectiveness of Neighbor2 Neighbor depends on the availability of extensive noisy image datasets. In contrast, [PERSON] et al. presented Self2 Self [28], which employs Bernoulli sampling and local convolution techniques to train with just a single noisy image without any clean reference. However, the extended training duration of Self2 Self limits its practical utility. Similarly, [PERSON] et al. introduced Deep Image Prior (DIP) [29], which utilizes the network architecture itself as implicit regularization. DIP relies solely on a single noisy image for online training, which doubles as the denoising process. This results in network parameters that are specifically tuned to the given noisy image, offering significant flexibility and practicality. Compared to Self2 Self, DIP more effectively preserves image details and considerably reduces the time required for training (and denoising). However, the time DIP takes to complete the denoising process remains substantially longer than that required by supervised models. Generally, such unsupervised denoising approaches, which do not require reference images, exhibit stronger generalization capabilities and practicality compared to their supervised counterparts. Nevertheless, in scenarios where clean image-noisy image pairs are available, the denoising performance of unsupervised models, which still rely solely on noisy images, lags significantly behind that of supervised models. While current deep-learning-based denoising models significantly outperform traditional techniques, both supervised and unsupervised approaches in this domain exhibit inherent limitations. Supervised models are typically limited to specific datasets, restricting their applicability across diverse scenarios. Conversely, unsupervised models, although less reliant on annotated data, still fall short regarding the denoising efficacy compared to supervised ones [23]. In our previous works [30; 31], we have significantly enhanced the DIP model's denoising capabilities. Nonetheless, the DIP model, known for its reliance on randomly initialized network parameters and network inputs during online training, boasts strong generalization capabilities but suffers from prolonged execution times due to excessive training iterations. This paper introduces a novel approach within the DIP framework, termed the Masked-Pre-training-Based Fast Deep Image Prior (MPFDIP) Denoising Model, aiming to preserve the DIP model's generalization capabilities and flexibility while reducing the number of iterations. Achieving this involves utilizing pre-trained model parameters to initialize the DIP model, thereby enhancing its execution efficiency. Specifically, the MPFDIP integrates a novel fast network model named FRformer (Fast Restormer). Built upon the core Transformer module from the established Restormer model, the FRformer incorporates techniques like sampling, residual learning, and refinement, replacing the traditional UNet backbone employed in the original DIP. Supported by masking technology, the pre-trained FRformer demonstrates a robust denoising performance across various noise types and intensities, considerably reducing both the number of model parameters and computational demand. By substituting DIP's UNet with FRformer, the convergence rate of unsupervised iterative training is accelerated, markedly improving the execution efficiency. Moreover, this study enhances the denoising performance further by refining the previously introduced multi-target image technology [30].