# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Non-Linear Motion Blur  
In this experiment, the Microsoft COCO dataset [32] is used as the training dataset. The Levin dataset [33] and the Sun dataset [34] will serve as test datasets. To accurately simulate nonlinear motion ambiguity in real scenarios, we randomly generate 1,000 nonlinear blur  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metrics** & **DUBLID [22]** & **DeblurGAN [8]** & **DeblurGAN-v2 [24]** & **DeepDeblur [6]** & **SRN [7]** & **SFNet [25]** & **Ours** \\ \hline PSNR(dB) \(\uparrow\) & 27.2115 & 26.1131 & 28.3261 & 25.3468 & 25.7333 & 29.3136 & **29.3616** \\ SSIM \(\uparrow\) & 0.8537 & 0.8429 & 0.8749 & 0.8536 & 0.8649 & 0.8819 & **0.8827** \\ MS-SSIM \(\uparrow\) & 0.9646 & 0.9628 & 0.9701 & 0.9411 & 0.9477 & 0.9738 & **0.9741** \\ LPIPS(ps) \(\downarrow\) & 0.3153 & 0.3198 & 0.2811 & 0.4530 & 0.4146 & 0.2373 & **0.2329** \\ Time(s) \(\downarrow\) & 0.71 & 0.94 & 0.69 & 3.77 & 2.91 & 0.69 & **0.64** \\ Param(M) \(\downarrow\) & **0.02** & 3.50 & 60.9 & 5.79 & 3.76 & 13.27 & 0.19 \\ Model Size(KB) \(\downarrow\) & **83** & 40.077 & 254.790 & 24.266 & 100.871 & 55.473 & 810 \\ FLOPs(B) \(\downarrow\) & N/A & 678.29 & 43.75 & 1760.04 & 1434.82 & 125.43 & **30.11** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of different models for removing linear motion blur.  
Figure 3: Examples of different models for removing linear motion blur. (**a**) Groundtruth (**b**) DUBLID [22] (**c**) DeblurGAN [8] (**d**) DeblurGAN-v2 [24] (**e**) DeepDeblur [6] (**f**) SRN [7] (**g**) SFNet [25] (**h**) Ours.  
kernels using the same method as [PERSON] et al. [22]. To enhance the model's robustness against noise, these blur kernels are applied to the images, with Gaussian noise added at a standard deviation of 0.01.  
The experimental results of various models for nonlinear blind deblurring on the Sun dataset and Levin dataset are presented in Tables 2 and 3, respectively. These tables clearly illustrate that the proposed model significantly outperforms other state-of-the-art models across both datasets, showcasing its effectiveness and robustness. In addition, Figure 4 provides a visual representation of the image restoration process for nonlinear motion blur, highlighting the qualitative improvements achieved by the proposed method. When compared to the alternatives, the proposed model demonstrates superior performance in effectively removing nonlinear motion blur. It not only eliminates the blur but also minimizes the ringing artifacts to the greatest extent possible. Furthermore, the model excels at accurately restoring letter edges and preserving intricate image details, ensuring that the restored images maintain high fidelity to the original content.  
data and 1111 pairs of test data, all captured with high-resolution and high-frame-rate cameras in dynamic or rapidly changing scenarios. These characteristics enable the dataset to encompass various types of blur, including non-uniform blur situations commonly encountered in real-world applications. Additionally, to enhance the proposed model's robustness to noise, Gaussian noise with a standard deviation of 0.01 was added to the images.  
Table 4 shows the experimental results of non-uniform blind deblurring by various models, while Figure 5 illustrates the restoration effects of non-uniform motion blur. Figure 6 presents the blind deblurring results on the Lai dataset [35]. In contrast, our model effectively restores complex blur patterns, significantly enhancing image sharpness and detail, while reducing the overall blur in the recovered images.