# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
## 4 Experiments
### Comparative Experiments
After conducting quantitative analysis of MDNCL's denoising performance, we selected several popular reference denoising methods, including BM3D, DnCNN, FFDNet, ADNet, BRDNet, and MWDCNN, for a visual qualitative comparison. We amplified and observed the differences among the methods by selecting the same areas of interest in the denoised images. Figures 4 and 5 showcase the grayscale image denoising performance. One image from each of the Set12 and BSD68 datasets was chosen for visual comparison at noise levels of 15 and 25, respectively. In Figure 4, the image \"House\" exhibits more preserved striped details when denoised by MDNCL. In Figure 5, MDNCL demonstrates the clearest outline of the castle compared to other methods. These visual comparisons indicate that MDNCL achieves the best visual performance and preserves more detailed features compared to the reference methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Image** & **C.man** & **House** & **Peppers** & **Starfish** & **Monarch** & **Airplane** & **Parrot** & **Lena** & **Barbara** & **Boat** & **Man** & **Couple** & **Average** \\ \hline Noise Level & & & & & & & & & & & & \\ BM3D [7] & 31.91 & 34.93 & 32.69 & 31.14 & 31.85 & 31.07 & 31.37 & 34.26 & 33.10 & 32.13 & 31.92 & 32.10 & 32.37/0.8953 \\ WNN \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Method** & \(\sigma=15\) & \(\sigma=25\) & \(\sigma=50\) & \(\sigma=75\) \\ \hline \multirow{8}{*}{**CBS [1]**} & CBM3D [7] & 33.52/0.925 & 30.71/0.872 & 27.38/0.767 & 25.74/ - \\  & CDnCNN-B [9] & 33.95/0.929 & 31.29/0.883 & 28.01/0.790 & - \\  & FFDNet [10] & 33.86/0.929 & 31.18/0.882 & 27.95/0.789 & 26.24/0.593 \\  & ADNet [20] & 34.02/0.933 & 31.34/0.889 & 28.05/0.797 & 26.33/0.606 \\  & BUIFD [37] & 33.65/0.930 & 30.76/0.882 & 26.61/0.777 & - \\  & AirNet [42] & 33.92/0.933 & 31.26/0.888 & 28.01/0.798 & - \\  & CMDNCL (ours) & **34.21**/**0.939** & **31.53**/**0.892** & **28.26**/**0.802** & **26.49**/**0.613** \\  & CMDNCL-B (ours) & 34.12/0.934 & 31.46/0.889 & 28.22/**0.802** & 26.41/0.609 \\ \hline \multirow{8}{*}{**CBS [1]**} & CBM3D [7] & 34.28/0.916 & 31.68/0.868 & 28.46/0.775 & 26.82/ - \\  & CDnCNN-B [9] & 34.73/0.920 & 32.23/0.876 & 29.02/0.791 & - \\  & FFDNet [10] & 34.55/0.922 & 32.11/0.878 & 28.99/0.794 & 27.25/0.733 \\  & ADNet [20] & 34.76/0.924 & 32.26/0.882 & 29.10/0.798 & 27.40/0.739 \\  & BUIFD [37] & 34.41/0.923 & 31.77/0.879 & 27.74/0.786 & - \\  & AirNet [42] & 34.68/0.924 & 32.21/0.882 & 29.06/0.799 & - \\  & MWDCNN [39] & 34.91/0.927 & 32.40/0.886 & 29.26/0.806 & 27.55/0.749 \\  & CMDNCL (ours) & **35.09**/**0.934** & **32.57**/**0.889** & **29.46**/**0.813** & **28.61**/**0.759** \\  & CMDNCL-B (ours) & 35.01/0.930 & 32.49/0.885 & 29.37/0.809 & 28.02/0.751 \\ \hline \multirow{8}{*}{**CBS [1]**} & CBM3D [7] & 34.06/0.915 & 31.66/0.874 & 28.51/0.793 & - \\  & CDnCNN-B [9] & 34.80/0.904 & 32.47/0.869 & 29.21/0.799 & - \\  & FFDNet [10] & 34.47/0.922 & 32.25/0.886 & 29.14/0.815 & - \\  & ADNet [20] & 34.93/0.927 & 32.56/0.894 & 29.36/0.825 & - \\  & BUIFD [37] & 33.84/0.901 & 31.06/0.847 & 26.20/0.733 & - \\  & AirNet [42] & 34.70/0.925 & 32.44/0.891 & 29.26/0.822 & - \\  & CMDNCL (ours) & **35.19**/**0.939** & **32.92**/**0.913** & **29.69**/**0.839** & - \\  & CMDNCL-B (ours) & 35.13/0.930 & 32.86/0.907 & 29.59/0.833 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average PSNR (dB) results of different methods on the CBSD68, Kodak24, and McMaster datasets (The superior results are emphasized in bold). Figure 4: Denoising results of “House” from Set12 with a noise level of 15. (**a**) clean image; (**b**) noisy image; (**c**) BM3D/34.93 dB; (**d**) DrCNN/34.99 dB; (**e**) FFDNet/35.07 dB; (**f**) ADNet/35.22 dB; (**g**) BRD-Net/35.27 dB; (**h**) MDNCL (Ours)/35.46 dB. Figure 5: Denoising results of images from BSD68 with a noise of level 25.