# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
## 5 Conclusions  
In this paper, we propose a multi-scale detail-noise complementary learning (MDNCL) network for image denoising. The two-branch architecture of MDNCL aims to strike a balance between noise reduction and detail preservation, with particularly noticeable performance gains at higher noise levels. Furthermore, the proposed FSU addresses issues related to redundant information and computational complexity during the denoising  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**256 \(\times\) 256**} & \multicolumn{2}{c}{**512 \(\times\) 512**} & \multicolumn{2}{c}{**1024 \(\times\) 1024**} \\  & & **Grayscale** & **Color** & **Grayscale** & **Color** & **Grayscale** & **Color** \\ \hline \multirow{4}{*}{CPU} & BM3D [7] & 0.446 & 0.589 & 2.274 & 3.661 & 9.687 & 12.788 \\  & TWSC [45] & 12.294 & 34.361 & 53.011 & 140.922 & 220.997 & 608.477 \\  & MCWNNM [46] & - & 62.732 & - & 278.073 & - & 1120.332 \\ \hline \multirow{8}{*}{GPU} & IRCNN [47] & 0.030 & 0.030 & 0.030 & 0.030 & 0.030 & 0.030 \\  & DnCNN-B [9] & 0.032 & 0.032 & 0.037 & 0.037 & 0.057 & 0.057 \\  & FFDNet [10] & 0.031 & 0.030 & 0.031 & 0.030 & 0.032 & 0.030 \\  & BUIFD [37] & 0.035 & 0.037 & 0.050 & 0.053 & 0.112 & 0.123 \\  & ADNet [20] & 0.031 & 0.033 & 0.035 & 0.045 & 0.051 & 0.093 \\  & SADNet [48] & 0.030 & 0.030 & 0.043 & 0.044 & 0.101 & 0.102 \\  & CycleISP [49] & - & 0.055 & - & 0.156 & - & 0.545 \\  & AirNet [42] & - & 0.143 & - & 0.498 & - & 2.501 \\  & VDN [50] & 0.144 & 0.162 & 0.607 & 0.597 & 2.367 & 2.376 \\  & AINDNet [51] & - & 0.531 & - & 2.329 & - & 9.573 \\  & VDIR [52] & - & 0.385 & - & 1.622 & - & 6.690 \\  & MDNCL & 0.279 & 0.316 & 0.574 & 0.697 & 2.013 & 2.174 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Running time (in seconds) of the compared models on three sizes of grayscale and color noisy images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameters** & **FLOPs** \\ \hline DnCNN [9] & 0.56 M & 0.891 G \\ ADNet [20] & 0.52 M & 0.832 G \\ ATDNet [53] & 9.45 M & - \\ RDUNet [12] & 166 M & 48 G \\ CTNet [54] & 49.03 M & 6.91 G \\ TBSN [44] & 12.97 M & 607.08 G \\ AirNet [42] & 8.72 M & - \\ MDNCL & 6.71 M & 9.34 G \\ \hline \hline \end{tabular}
\end{table}
Table 9: Complexity of different denoising methods.  
process, resulting in more effective preservation of detailed features. To evaluate the denoising capabilities of MDNCL, we conducted experiments on various datasets for both grayscale and color images, as well as ablation experiments on the Set12 dataset. The obtained experimental results consistently reinforce the strong denoising performance of MDNCL. However, the proposed MDNCL also has some limitations. This method is suitable for single noise and real-world noise, but it may not be effective for complex mixed noise and noise in complex environments. We will further investigate combining the idea of generative models with MDNCL to improve denoising performance in future work.  
Conceptualization, Y.C. and J.J.; methodology, J.J. and M.S.; validation, M.S.; formal analysis, M.S. and Y.C.; investigation, Y.C. and J.J.; resources, Y.C.; writing--original draft preparation, M.S. and Y.C.; writing--review and editing, Y.C. and M.S.; visualization, J.J.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62001236, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 20 KJAS20003, and in part by the Six Talent Peaks Project of Jiangsu Province under Grant JY-051.  
Not applicable.