# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 4 Experimental Results
### Ablation Experiments  
To verify whether or not the image quality of input and target images has a significant impact on unsupervised fine-tuning training, we conducted a series of experiments. Specifically, we choose four representative noise reduction methods (FFDNet, DAGL, DRUNet, and SwinIR) as well as our own Stage1 model (i.e., U-Transformer) as a preprocessor to denoise the given noisy images from the SET12 dataset. Each image has been added with Gaussian noise with a standard deviation of 25. The obtained denoised image, \(x_{p}\), is then input into the fine-tuning network to boost the image denoising effect. Note that both the preprocessed image, \(x_{p}\), and the noisy image, \(y\), are used as the target images, and the final image, \(x_{f}\), is generated by fine-tuning the Stage2 model. We meticulously recorded the PSNR values of the preprocessed images and the final denoised images obtained from various noisy images and calculated their averages. The experimental results are shown in Table 1. It can be observed from Table 1 that there is a positive correlation between the final denoising effect of the final denoised images and the image quality of the preprocessed images. Through the analysis of data correlations, it is evident that the PSNR data for rows \(x_{p}\) and \(x_{f}\) exhibit a strong positive linear relationship, with a Pearson's correlation coefficient of 0.99. This suggests a close association between the quality of the output image, \(x_{f}\), and that of the preprocessed image, \(x_{p}\). More specifically, the higher quality for the preprocessed image, \(x_{p}\), corresponds to a higher PSNR value for the output image, \(x_{f}\). Furthermore, the data in Table 1 also indicates that the proposed Stage1 model is expected to outperform current mainstream denoising models in terms of PSNR. It should be noted that, while using other models, such as DnCNN, as a preprocessing model can lead to larger performance improvements (even up to 0.3 dB), the final denoising result is still not as good as our U-Transformer (Stage1) due to the lower PSNR values of its input, \(x_{p}\). Therefore, in the unsupervised fine-tuning stage, we choose to use the preprocessed images, \(x_{p}\), generated by U-Transformer as input and target images for fine-tuning the Stage2 model. Figure 8: Ten representative test images in the SIDD dataset. Figure 7: Ten representative test images in the Urban100 dataset. To further evaluate the performance improvement achieved by using noisy images and preprocessed images as target images, we conducted tests on various scenarios of the loss functions employed during the training of the Stage2 model. From the data in Table 2, it can be observed that the proposed hybrid loss function ensures the optimal denoising effect during the fine-tuning stage. The reason behind this is that, although preprocessed images exhibit high image quality, there is inevitably some smoothing of pixel values after the preprocessing stage. On the other hand, noisy images, while containing more noise, may have pixels that are not as significantly altered. Hence, there exists a certain complementarity between preprocessed and noisy images. As target images, this complementarity facilitates the Stage2 model to converge to more reasonable positions, resulting in a better denoising effect. As mentioned earlier, the noisy image still plays a crucial role as the primary target image in the loss function. To validate this role, we conducted a series of experiments on the images from the SET12 dataset with noise levels of 15, 25, and 50, respectively. Table 3 lists the specific experimental weight values for noise levels of 15, 25, and 50, which is set to 0.2, 0.4, and 0.8 through trial and error, respectively. The following experiments all use the parameters listed in Table 3. The above experiments indicate that, as the severity of noise increases, the Stage2 model relies more on information from pixels in the noisy image that are not affected by noise for image restoration.