# Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity
## 3 Proposed Algorithm
### Compare with Filter based Methods  
In this section, the results are compared from proposed method and other filter-based methods, such as wiener and bilateral filter, on two datasets. Wiener and bilateral filters are typical methods for de-blurring. Filter-based methods are applied to blurred image to eliminate the blur noise in the image. However, they do not reflect non-linearity of data and perceptual style information because they cannot learn the features of the image.  
The results of the comparison of the proposed method with other filter-based methods using two different datasets are given in Tables 1 and 2. The performances of methods are evaluated by means of peak signal to noise ratio (PNSR) and structural similarity measure (SSIM). The results of PSNR and SSIM on GOPRO Large dataset are measured in the top 50 averages, and the result on Kohler dataset are measured in the top 5 averages.  
As a result, the proposed method, which minimizes distributions between two images, shows better results in PSNR and SSIM than other filter-based methods, which minimizes pixel-based differences between two images.  
Figures 16 and 17 show the output images of filter-based methods and proposed method using GOPRO Large dataset. With the proposed method, the blur noise in the image is removed, and the output images are similar to the sharp image. Also, the output images using the Kohler dataset are shown in Figure 18. This dataset is made by applying artificial blur noise rather than natural blur noise. The proposed network reconstructs de-blurred image better than the filter-based methods in SSIM. This is because the network captures the perceptual style and complex features to reconstruct the de-blurred image, and the filter-based methods and proposed method have a different form to minimize value.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Compare Proposed Method to Filter Method**} \\ \cline{2-3}  & **PSNR** & **SSIM** \\ \hline Bilateral filter [1] & 26.67 & 0.93 \\ Wiener filter [2] & 28.58 & 0.92 \\ Proposed method & **33.29** & **0.98** \\ \hline \end{tabular}
\end{table}
Table 1: PSNR and SSIM on GOPRO Large dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Compare Proposed Method to Filter Method**} \\ \cline{2-3}  & **PSNR** & **SSIM** \\ \hline Bilateral filter [1] & **25.24** & 0.84 \\ Wiener filter [2] & 24.51 & 0.84 \\ Proposed method & 23.29 & **0.86** \\ \hline \end{tabular}
\end{table}
Table 2: PSNR and SSIM on Kohler dataset.  
Figure 16: Object images from comparing proposed method with filter-based methods: (**a**) Wiener filter (**b**) Bilateral filter (**c**) Proposed method (**d**) Original sharp image.  
Figure 17: Background images from comparing proposed method with filter-based methods: (**a**) Wiener filter (**b**) Bilateral filter (**c**) Proposed method (**d**) Original sharp image.  
Figures 19 and 20 are generated images from proposed method and WGAN-GP with content loss on GOPRO Large dataset, and Figure 21 is generated image on Kohler dataset. Both methods generate sharp images since they use feature map in VGG16; however, output image of WGAN-GP with content loss shows block effect which looks like a check pattern. With the de-blurred image of proposed method using style loss, the block effect is removed, and detailed edge in sub-part of image, e.g., branch, leaf and background, are preserved. Consequently, proposed method generates de-blurred images that are more natural to human eyes.  
Figure 20: Background images from comparing proposed method with WGAN-GP using content -based methods: (**a**) WGAN with content loss (**b**) Proposed method (**c**) Original sharp image.