# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
## 4 Experiments
### Ablation Experiment  
* **Role of network framework:** To verify the effectiveness of complementary learning branches and the FSU module in MDNCL, we conducted ablation experiments on the Set12 dataset. For complementary learning branches, MDNCL was compared to a corresponding network with only one branch. For the FSU module, the MDNCL was compared to a corresponding network without an FSU. Specifically, we compared MDNCL with MDNCL-NLB, which only learns image details; MDNCL-DLB, which only learns noise; and MDNCL-FSU, which does not have an FSU. To ensure a fair comparison, all networks were trained using the same framework shown in Figure 1. The quantitative results of ablation experiments are shown in Table 6, highlighting the effectiveness of MDNCL.  
Figure 8: Denoising results of one image from the McMaster dataset with a noise level of 50. (**a**) clean image; (**b**) noisy image; (**c**) DnCNN/31.13 dB; (**d**) ADNet/31.84 dB; (**e**) FFDNet/31.82 dB; (**f**) CMDNCL (Ours)/32.08 dB.  
Figure 9: Denoising results on SIDD. The noisy images are arranged at the top, and the denoised images are presented at the bottom.  
For visual comparison, Figure 10 shows the results of the ablation experiment. It can be concluded that when a single branch is used to learn detailed features or noise mappings, the denoising results suffer from varying degrees of detail feature loss. However, when the FSU module is removed, the feature artifacts appear in the denoised image, and the noise is not completely removed. Overall, the strong denoising performance of MDNCL is achieved through the joint action of the two branches and the FSU module.  
* **Role of loss function:** The \(\mu\) hyperparameter in the loss function was determined by a series of experiments. We conducted experiments on the Set12 dataset, changing the value of the hyperparameter to 0.4, 0.6, 0.8, 1.0, and 1.2. To ensure fair comparisons, all networks were trained using the same framework shown in Figure 1, and quantitative results of the ablation experiments are shown in Table 7.  
As can be seen from Table 7, the best denoising performance was obtained by setting the \(\mu\) hyperparameter to 1.0. When the value of \(\mu\) is greater than 1.0, the network pays more attention to removing noise and ignores the retention of details. When \(\mu\) is less than 1, the network is not able to effectively remove noise. Therefore, it is important to strike an balance between detail retention and noise removal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\sigma=15\) & \(\sigma=25\) & \(\sigma=50\) \\ \hline MDNCL-DLB & 32.46/0.8913 & 30.19/0.8589 & 27.17/0.7821 \\ MDNCL-NLB & 31.93/0.8794 & 29.82/0.8497 & 26.89/0.7690 \\ MDNCL-FSU & 28.89/0.8623 & 29.48/0.8446 & 24.11/0.7413 \\ MDNCL & **33.14/0.9065** & **30.80/0.8679** & **27.67/0.7955** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average PSNR (dB)/SSIM results of ablation experiments on Set12 (The superior results are emphasized in bold).  
Figure 10: Denoising results of one image from Set12 with a noise level of 15. (**a**) clean image; (**b**) noisy image; (**c**) MDNCL-NLB/30.19 dB; (**d**) MDNCL-DLB/31.27 dB; (**e**) MDNCL-FSU/28.79 dB; (**f**) MDNCL/32.08 dB.  
To more intuitively demonstrate why setting the \(\mu\) hyperparameter to 1.0 is a reasonable choice, we compared denoised images with different \(\mu\) values. As can be seen in Figure 11, when \(\mu\) is less than 1, the zoomed-in detail areas, such as the white feathers, still contain a significant amount of noise. This indicates that the denoising effect is insufficient and smoothing is inadequate. On the other hand, when \(\mu\) is greater than 1, the noise is reduced, but the smoothing is too strong, leading to a loss of detail information. Through this comparison, we can see that when \(\mu\) equals 1.0, it achieves a good balance between noise removal and detail preservation.