# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 4 Experimental Results and Discussion
### Discussion  
An extensive discussion of our proposed global-local blur disentangling network for dynamic scene deblurring is made in this section to provide further insights into the potential of further work. * Dynamic scene deblurring and its challenge In dynamic scene images, the complex blurs caused by various sources, such as object motion, camera shake, and scene depth variation. Different types of blurs are randomly coupled together with different parameters. For camera shake, it results from global blur. The blur kernel may be uniform everywhere. For object motion and scene depth variation, the blur kernel is local, which varies with respect to spatial position. Therefore, variation is the major challenge in dynamic scene image deblurring. We proposed to handle the complex variation of blur kernels with disentangling analysis. The different types of blur are roughly divided into a global and a local blur components. It is reasonable to handle the global and local blurs with different deblurring schemes. Intuitively, the idea of disentangling analysis provides adaptive mechanism to handle the variation of blur kernels. * Disentangling blur analysis and its implementation The motivation of our proposed method is to disentangle the blur to global and local components. The way to implement the disentangling operation depends on \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR (dB)** & **SSIM** & **Time (s)** \\ \hline [PERSON] et al. [18] & 28.3 & 0.92 & 3.09 \\ \hline DeblurGAN [20] & 27.2 & 0.95 & 0.97 \\ \hline DeblurGAN-v2 [21] & 29.6 & 0.93 & 7.15 \\ \hline BAG [34] & 29.4 & 0.89 & 1.13 \\ \hline [PERSON] et al. [43] & 29.67 & 0.93 & – \\ \hline [PERSON] et al. [44] & 29.81 & 0.94 & – \\ \hline [PERSON] et al.[38] & 30.92 & 0.94 & 1.60 \\ \hline [PERSON] et al. [24] & 30.26 & 0.94 & – \\ \hline [PERSON] et al. [36] & 31.42 & 0.94 & – \\ \hline Ours & 32.27 & 0.92 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNRs, SSIMs, and running time on the GoPro dataset. two points, viz. the dual-branch architecture and the phased training scheme. The dual-branch network provides a framework to disentangle the two types of blur components. The phased training scheme forces the different branches to extract the global and local blur features, respectively. The advantages of our dual-branch network include (1) avoiding error accumulation and (2) partially interpretable, while the disadvantage is that it cannot be trained in an end-to-end manner. * Potential alternative implementation There are alternative approaches, such as the attention mechanism, to implement the idea of blur disentangling analysis. Using attention mechanism to disentangle blur features will be an interesting exploration. The attention-based blur disentangling network will depend on data correlation. Therefore, there is potential to discover more interesting disentangled blur factors. The drawback of the attention-based network is more parameters are involved, which leading to more training data required. * Limitations on performance The proposed dual-branch network benefits from the realistic ground-truth blur dataset, GoPro [18]. Compared to kernel convolution, integrating over time to the high frame rate videos provides more realistic dynamic blurry images. It enables efficient supervised deep learning and rigorous evaluation. However, there are two limitations that affect the performance of deblurring for real-world images. One is the inaccurate camera response function (CRF). As mentioned in Reference [18], there is no efficient CRF estimation algorithm available, the gamma correction with \(\gamma=2.2\) is used to estimate the CRF. Another one is domain mismatching. If the input blurry images are far away from that of the training set, the performance may decline. Domain adaptation methods may be helpful in such case.