# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 4 Experimental Results and Discussion
### Comparisons with State-of-the-Art Deblurring Methods  
To measure the effective of the proposed method, we compared our method with 9 other latest dynamic scene image deblurring methods in this subsection. They are method proposed by [PERSON] et al. [18], DeblurGAN [20], DeblurGAN-v2 [21], BAG [34], and the method proposed by [PERSON] et al. [38]. The method of [PERSON] et al. [18] uses an end-to-end network to restore images and achieves a good deblurring effect. DeblurGAN [20] applies generative adversarial networks to image deblurring tasks, and this method can restore image details better. DeblurGAN-v2 [21] can recover rich edges and contours. The BAG [34] and [PERSON]'s method [38] are the latest dynamic scene deblurring algorithms, which achieve good subjective and objective results on the GoPro dataset. [PERSON]'s method [43] generalizes better to handling real-world motion blur. [PERSON]'s method [44] and [PERSON]'s method [36] can better restore blurry images in dynamic scenes. [PERSON]'s method [24] restored the blurry images with more semantic details. All objective results are reported in the papers. **Subjective evaluation. ** Figure 4 shows the subjective results of some of the compared methods. We can see that our method can restore the blurry image in high dynamic scene, and obtain the restored image with clear edges. It can be seen from the first image that the result of [PERSON]'s method [38] is blur in the zoom in regions. The characters on the wall cannot be recognized due to global blur. The result of DeblurGAN-v2 [21] is likely to cause averaging of restored images for local motion blur. Our method can better restore the dynamic blur of the sleeves and the uniform blur of the background text, which \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **LB-Net** & **DB-Net** & **DBAF-Net** \\ \hline Local Branch & ✓ & ✓ & ✓ \\ Global Branch & & ✓ & ✓ \\ Branch Attention & & & ✓ \\ \hline PSNR (dB) & 31.07 & 31.77 & 32.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average Peak Signal to Noise Ratio (PSNR) Results of ablation experiments. Figure 3: Subjective results of ablation experiments. From left to right are the blurry image, restoration results of the Local-Branch-Net (LB-Net), Dual-Branches-Net (DB-Net), Dual-Branch Attention Fusion Net (DBAF-Net), and the ground truth. indicated that our method can handle both global and local blur. We have released the representative results of the proposed method in Figure 4 through the following website: [[https://doi.org/10.6084/m9.figshare.14068721.v1](https://doi.org/10.6084/m9.figshare.14068721.v1)]([https://doi.org/10.6084/m9.figshare.14068721.v1](https://doi.org/10.6084/m9.figshare.14068721.v1)) accessed on 20 February 2021. Figure 4: Subjective results of the GoPro test images. From top to bottom are the blurry images, the results of DeblurGAN (GAN = Generative Adversarial Network) [20], DeblurGAN-v2 [21], BAG [34], [PERSON]’s method [38], our method, and the ground truth. Note that the ground truth images are taken by a high-speed global shutter camera as shown in the last row in Figure 4. According to the sampling theorem, most motion blur results in the shutter frequency is lower than 2 times the object motion. The proposed technique is used to relieve the frequency confusion effects. **Objective evaluation. ** The objective experimental results of different methods are listed in Table 2. From the results we can see that our proposed method achieved 32.27 dB, which is a significant improvement in the objective results. The SSIM is comparable to other methods. **Computation complexity. ** To evaluate the computational complexity of different methods, we have tested several compared methods with author-released source codes. The running time results in seconds for a 720 \(\times\) 1280 image are listed in Table 2. We can see that the running time of our method is 0.72 s. It is the fastest method. The basic operation unit of our proposed network is a \(3\times 3\) convolution on pixels.