# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Comparison with Existing Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Indoor**} & \multicolumn{3}{c}{**Outdoor**} & \multicolumn{3}{c}{**Combine**} & \multirow{2}{*}{**Param \(\downarrow\)**} & \multirow{2}{*}{**FLOPs \(\downarrow\)**} \\ \cline{2-2} \cline{6-11}  & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** & \\ \hline BlurryInput & 26.37 & 0.808 & 0.033 & 21.53 & 0.645 & 0.060 & 23.89 & 0.725 & 0.471 & - & - \\ EDDB [15] & 25.77 & 0.772 & 0.040 & 21.25 & 0.599 & 0.058 & 23.45 & 0.683 & 0.049 & - & - \\ DMENet [4] & 25.70 & 0.789 & 0.036 & 21.51 & 0.655 & 0.061 & 23.55 & 0.720 & 0.049 & 26.71 M & - \\ JNB [16] & 26.73 & 0.828 & 0.031 & 21.10 & 0.608 & 0.064 & 23.84 & 0.715 & 0.048 & - & - \\ DPDNet-S [9] & 26.54 & 0.816 & 0.031 & 22.25 & 0.682 & 0.056 & 24.34 & 0.747 & 0.044 & 34.52 M & 109.49 G \\ MDPNet [17] & 28.02 & 0.841 & 0.027 & 22.82 & 0.690 & 0.052 & 25.35 & 0.763 & 0.040 & 46.86 M & 269.98 G \\ RPD+ [36] & 28.10 & 0.843 & 0.027 & 22.82 & 0.704 & 0.053 & 25.39 & 0.772 & 0.040 & 27.51 M & 272.64 G \\ Uformer-B [11] & 28.23 & **0.860** & **0.026** & **23.10** & **0.728** & **0.051** & **25.65** & **0.795** & **0.039** & 50.88 M & 89.46 G \\ \hline CMDNet & **28.37** & 0.852 & 0.025 & 22.86 & 0.708 & 0.052 & 25.54 & 0.778 & **0.039** & **9.74 M** & **39.39 G** \\ (ours) & \multicolumn{1}{c}{} & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of different methods on the DPDD dataset (containing 37 indoor and 39 outdoor scenes). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE (x10) \(\downarrow\)** \\ \hline Blurry Input & 22.33 & 0.633 & 0.513 \\ JNB [16] & 22.36 & 0.635 & 0.511 \\ EBDB [15] & 22.38 & 0.638 & 0.509 \\ DMENet [4] & 22.41 & 0.639 & 0.508 \\ DPDNet-S [9] & 22.67 & 0.666 & 0.506 \\ GKMNet [8] & 24.30 & 0.716 & 0.413 \\ \hline CMDDNet (our) & **24.62** & **0.726** & **0.405** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of model performance on the RealDOF dataset. icant reduction in the number of parameters indicates that the method can greatly improve the operation efficiency while maintaining good performance. Therefore, compared with Uformer-B, the proposed method achieves a better balance between performance and computational complexity. The experimental results on the RealDOF dataset show that the index numbers of PSNR, SSIM and MAE are 24.62 dB, 0.726 and 0.405, respectively. (Due to the small difference between the MAE indicators in the RealDOF dataset, the value is expanded by ten times to reflect the difference.) Compared with Gaussian hybrid blur kernel estimation network (GKMNet), the PSNR value and SSIM value of this model are increased by 0.32 dB and 0.01. The effectiveness of the model in removing defocusing blur in real scenes is proved. In addition, compared with the traditional defocusing blur kernel estimation method, this model overcomes the limitation of kernel estimation and turns to the data-driven deep learning method, showing significantly better performance.