# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 2 Related Work
### Dip  
Deep image prior (DIP), introduced by [PERSON] et al. [39], is an unsupervised learning technique that focuses on optimizing network parameters to adapt to input noisy images for comprehensive reconstruction and denoising purposes. This optimization process is uniquely based on a single noisy image as the training target, leveraging the implicit constraints inherent in deep learning for online training. The training procedure can be expressed as follows:  
\[\theta^{*}=\underset{\theta}{\arg\min}E(f_{\theta}(z);y) \tag{4}\]  
\[x=f_{\theta}(z) \tag{5}\]  
where \(f\) refers to a neural network, \(\theta\) represents the network parameters (initially randomly initialized), and \(z\) is a tensor following a random distribution (similar to the input of a generative adversarial network). Through training, the optimal parameter solution, denoted as \(\theta^{*}\), is obtained, and \(x^{*}\) corresponds to the optimal image output generated by the network. Notably, the DIP starts with the random initialization of a convolutional neural network and iteratively adjusts its parameters by minimizing a loss function to closely align the generated image with the input noisy image. This loss function typically involves calculating pixel differences between the two images. By iteratively optimizing the parameters, the DIP effectively produces reconstructions that closely resemble their original counterparts.  
In the domain of image processing, the DIP method has emerged as a powerful approach for unsupervised learning in image restoration. Its ability to recover images without relying on extensive training datasets makes it highly adaptable for image denoising. The efficacy of the DIP technique dynamically adapts to different application scenarios and intrinsic image characteristics, allowing for optimal performance in diverse contexts. However, when compared to supervised models, the DIP falls short in the presence of a substantial amount of labeled training data. Moreover, as shown in Figure 1, utilizing a five-layer UNet network [52] to obtain multi-scale information during the iterative DIP process leads to reduced iteration speed. While this approach enhances denoising effectiveness, it also increases computational complexity and time costs. Additionally, the DIP method gradually learns the target image, progressively approaching the desired outcome during the denoising process. The quality of the target image directly impacts the denoising effect. Extensive experimental results indicate that using a poor-quality image as the target image results in a wide network search range and suboptimal denoising outcomes (for specific details, refer to the ablation experiments). To achieve superior denoising results, it is advisable to consider using higher-quality target images or employing other preprocessing techniques to enhance the image quality of the target image. In summary, although the DIP method offers significant advantages in unsupervised learning for image restoration, its performance can be further improved by using a higher-quality preprocessed image as the target image instead of directly using the noisy image. These enhancements are likely to yield more efficient and effective image denoising results.