# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Ablation Study
#### 4.6.4 Qualitative Comparison of Different Deblurring Methods  
A qualitative comparison was performed on the DPDD public dataset, selecting representative indoor and outdoor blurred images for visualization of the restoration results. The results are shown in Figure 7, where panels (b), (d), (f), and (h) are zoomed-in views of the local details of panels (a), (c), (e), and (g), respectively. The first column shows the input images with spatially varying blur (Input), and the last column displays the corresponding ground truth sharp images (Sharp). The middle rows present the restoration results of different methods, including the proposed CMDDNet. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Dim** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** & **Param \(\downarrow\)** \\ \hline \multirow{3}{*}{DPDD} & 24 & 25.42 & 0.774 & 0.039 & **4.49 M** \\  & 36 & 25.54 & 0.778 & 0.039 & 9.74 M \\  & 48 & **25.57** & **0.780** & **0.038** & 16.99 M \\ \hline \multirow{3}{*}{RealDOF} & 24 & 24.39 & 0.717 & 0.041 & **4.49 M** \\  & 36 & **24.62** & **0.726** & **0.040** & 9.74 M \\ \cline{1-1}  & 48 & 24.51 & **0.726** & **0.040** & 16.99 M \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation study on dimension size using the DPDD and RealDOF datasets. Through visual comparison, CMDDNet demonstrates superior deblurring performance, producing results closer to the ground truth. Specifically, MDPNet [20] exhibits significant color distortion and over-sharpening artifacts, while RPDP+ [32] generates noticeable visual noise in detail regions. In contrast, CMDDNet excels in handling complex blurring scenes, particularly under light variations and spatially inconsistent blur as evidenced by sharper edges and more natural textures in the red-boxed zoomed-in areas. Figure 6: Ablation study of the global feature extraction module on the mini RealDOF dataset. Figure 7: Qualitative comparison of different deblurring methods on the DPDD dataset. Subfigures (**a**,**c**,**e**,**g**) show the deblurring results of different methods, while subfigures (**b**,**d**,**f**,**h**) are the corresponding zoomed-in views of the local details in (**a**,**c**,**e**,**g**), respectively. Similarly, a qualitative comparison was conducted on the RealDOF public dataset as shown in Figure 8. Since RealDOF contains only a test set, the model was trained on the DPDD dataset and tested on the RealDOF dataset, thereby reflecting the model's generalization capability to some extent. Similar to Figure 7, the results are presented in the form of original images and locally magnified views, with a focus on comparing the proposed method with GKMNet [8], which exhibits the closest performance to our approach. From the figure, it can be observed that the restoration results of GKMNet and CMDDNet are visually similar. However, GKMNet exhibits varying degrees of tone overflow, such as yellow or green artifacts, particularly in edge details or regions with bright lighting in the original images. This phenomenon may be attributed to factors such as blur kernel estimation errors, over-enhancement by attention modules, and insufficient dynamic range processing. Although GKMNet employs a pixel-wise Gaussian Kernel Mixture (GKM) model to represent spatially varying defocus blur kernels with high precision, its blur kernel estimation may still incur errors under inconsistent blur conditions, leading to color distortion in the restored results. In contrast, CMDDNet adopts a data-driven approach by learning the mapping between defocused and sharp images, thereby overcoming the limitations of traditional blur kernel estimation methods under inconsistent blur conditions. This enables more natural color consistency and detail restoration. In summary, the visual results demonstrate the significant advantages of CMDDNet in handling image blur, avoiding tone overflow, and preserving natural textures.