# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
## 4 Experiments
### Comparative Experiments  
To assess the denoising performance of MDNCL, we conducted a comparison with several advanced denoising methods. The reference methods used in the comparison were BM3D [7], WNNM [35], DnCNN [9], FFDNet [10], ADNet [20], BRDNet [36], BUIFD [37], CDNet [38], MWDCNN [39], DRFENet [40], CBDNet [41], AirNet [42], HI-GAN [43], and TBSN [44]. Among these methods, BM3D [7] and WNNM [35] are traditional denoising techniques, while the remaining approaches are state-of-the-art denoising methods based on deep learning. For quantitative evaluation, we utilized the PSNR and SSIM metrics to measure the performance of MDNCL relative to the other methods. During the grayscale testing of MDNCL, we employed the Set12 and BSD68 datasets to evaluate its denoising performance. As illustrated in Tables 2 and 3, MDNCL achieved the highest average PSNR and SSIM results on Set12 and BSD68 at noise levels of 15, 25, and 50. Specifically, when examining the \"Barbara\" image from the Set12 dataset (Table 3), MDNCL's denoising performance ranked second, just behind WNNM. MDNCL also demonstrated impressive denoising performance in blind denoising (MDNCL-B) compared to other state-of-the-art denoising methods. For instance, the PSNR and SSIM results of MDNCL-B on the Set12 dataset surpassed those of both DnCNN and DRFENet, as shown in Table 3. Detailed PSNR results for each image and the average PSNR and SSIM scores for each method on the Set12 dataset at three different noise levels can be found in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Batch size & 16 \\ Epochs & 90 \\ Initial learning rate & 0.0001 \\ \(\beta_{1}\) & 0.9 \\ \(\beta_{2}\) & 0.999 \\ \(\mu\) & 1 \\ Xavier initialization & Draw weights from a normal distribution with a mean of 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\sigma=15\) & \(\sigma=25\) & \(\sigma=50\) \\ \hline BM3D [7] & 31.07/0.8729 & 28.57/0.8042 & 25.62/0.6817 \\ WNNM [35] & 31.37/0.8777 & 28.83/0.8103 & 25.87/0.6975 \\ DnCNN [9] & 31.72/0.8902 & 29.23/0.8288 & 26.23/0.7180 \\ FFDNet [10] & 31.63/0.8901 & 29.19/0.8298 & 26.29/0.7262 \\ ADNet [20] & 31.74/0.8910 & 29.25/0.8285 & 26.29/0.7211 \\ CDNet [38] & 31.74/0.8915 & 29.28/0.8312 & 26.36/0.7229 \\ BRDNet [36] & 31.72/0.8910 & 29.24/0.8295 & 26.28/0.7232 \\ DRFENet [40] & 31.76/0.8920 & 29.26/0.8301 & 26.29/0.7230 \\ MWDCNN [39] & 31.77/0.8919 & 29.28/0.8305 & 26.29/0.7229 \\ MDNCL (ours) & **31.82/0.8923** & **29.30/0.8316** & **26.38/0.7242** \\ MDNCL-B (ours) & 31.76/0.8919 & 29.25/0.8304 & 26.31/0.7234 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNR (dB)/SSIM results of different methods on BSD68 (The superior results are emphasized in bold). To assess the denoising performance of MDNCL on noisy color images, we conducted evaluations using the CBSD68, Kodak24, and McMaster datasets. As indicated in Table 4, CMDNCL achieved the highest PSNR results across all three datasets and surpassed those of existing reference methods. When the noise level was as high as 75, CMDNCL still obtained the best denoising index, which proves the superior denoising ability of CMDNCL. Furthermore, in the context of blind denoising on color images, we observed that MDNCL-B also demonstrated impressive denoising performance. It effectively removed noise from color images, further affirming the effectiveness of CMDNCL in various denoising scenarios. To evaluate the denoising performance of MDNCL in removing real-world noise, we conducted tests using the SIDD validation and DND datasets. As illustrated in Table 5, MDNCL achieved the highest PSNR and SSIM results, indicating its effectiveness in removing real-world noise. This demonstrates that MDNCL is well-suited for real-world denoising applications.