# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Experimental Results with Other Denoising Methods  
Next, we compared the performance of the proposed model and the nine other typical denoisers. We obtained the average PSNR results of these models on 10 commonly used images with different noise levels ranging from 10 to 60 with a step of 10 and calculated the overall average at the end. From Table 5, we can obtain the following observations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise Level** & \(\mathbf{\sigma=10}\) & \(\mathbf{\sigma=20}\) & \(\mathbf{\sigma=30}\) & \(\mathbf{\sigma=40}\) & \(\mathbf{\sigma=50}\) & \(\mathbf{\sigma=60}\) \\ \hline dataset & \multicolumn{5}{c}{common10} \\ \hline CsNet & 34.62 & 31.40 & 29.75 & 28.55 & 27.61 & 26.79 \\ SPD & 34.76 & 31.37 & 29.67 & 28.41 & 27.54 & 26.77 \\ U2 Fusion & 29.66 & 28.52 & 27.63 & 26.83 & 26.24 & 25.67 \\ MEFNet & 35.04 & 31.56 & 29.81 & 28.54 & 27.58 & 26.79 \\ Proposed & **36.46** & **31.81** & **30.82** & **28.78** & **28.49** & **27.03** \\ \hline dataset & \multicolumn{5}{c}{BSD} \\ \hline CsNet & 33.59 & 29.67 & 27.90 & 26.75 & 25.91 & 25.20 \\ SPD & 33.57 & 29.59 & 27.80 & 26.60 & 25.80 & 25.13 \\ U2 Fusion & 28.47 & 27.14 & 26.14 & 25.36 & 24.75 & 24.25 \\ MEFNet & 34.19 & 30.03 & 28.06 & 26.80 & 25.92 & 25.25 \\ Proposed & **34.22** & **30.82** & **28.16** & **26.91** & **26.04** & **25.35** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average performance of various combination strategies for different noise levels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{L_{\text{IE}}}\) & \(\mathbf{L_{\text{TV}}}\) & \(\mathbf{L_{\text{MSE}}}\) & \(\mathbf{L_{\text{MSE}}+L_{\text{TV}}+L_{\text{IE}}}\) \\ \hline \(\sigma=10\) & 35.50 & 35.17 & 36.44 & **36.46** \\ \(\sigma=30\) & 30.03 & 29.85 & 30.81 & **30.82** \\ \(\sigma=50\) & 27.79 & 27.48 & 28.45 & **28.49** \\ Average & 31.11 & 30.83 & 31.90 & **31.92** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average PSNR values obtained by selecting different combinations of loss functions on 10 commonly used test images corrupted by different noise levels. First, although CsNet combined denoised images generated by both model-based and learning-based denoisers, its average denoising effect is inferior to that of the mainstream denoisers, such as VDNet, due to its coarse combination granularity. Second, our method outperformed competing models by a considerable margin and yielded the best average PSNR results. While image denoising methods have shown encouraging achievements over the last decade, note that achieving even a slight performance increase has become more challenging for numerous denoising methods. However, the PSNR score of our method has surpassed that of the learning-based and model-based methods FFDNet and BM3D by 0.18 (on average) to 0.54 dB and 0.54 (on average) to 0.69 dB, respectively. Since only a few methods surpass the benchmark BM3D method by more than 0.3 dB on average [40; 41], this is a significant improvement. To demonstrate the robustness of our method, we performed comparisons by randomly selecting 50 images from the BSD, making the process of image denoising more challenging. The quantitative comparison with the competitors on the BSD is summarized in Table 6. On this dataset, the denoising effects of each denoiser show a decline to different degrees compared to the average values obtained on the first dataset. Nevertheless, it is also apparent that our method still achieved a substantial improvement over others. The PSNR results of the ten denoisers are shown in Table 6. Specifically, we observed under the average value that the PSNR score of our method surpassed that of BM3D by 0.83 dB, FFDNet by 0.30 dB, NCSR by 0.66 dB, DNCNN by 0.27 dB, WNNM by 0.43 dB, VDNet by 0.19 dB, CSNet by 0.29 dB, SPD by 0.38 dB, U2 Fusion by 3.71 dB and MFNet by 0.25 dB. Therefore, our proposed method brings significant improvements to the PSNR results. The experimental results showed that our method outperformed the other state-of-the-art denoisers on two representative datasets.