# Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity
## 1 Introduction  
De-blurring is one of the steadily studied techniques in image processing fields and aims to improve the sharpness of an image by eliminating blur noise. In Figure 1, the de-blurring method is used to transform the blurred image to the sharp image. To remove blur noise, many de-blurring techniques were researched, e.g., the blind deconvolution algorithm, bilateral and Wiener filter among others [1,2]. Figure 1: Blurred and sharp image of GOPRO dataset. In recent years, deep learning methods have been adapted to image processing fields, showing better performance than the previous methods. Likewise, many researchers have applied deep learning methods, e.g., convolutional neural network (CNN) and generative adversarial network (GAN), in order to resolve blur noise [3; 4; 5]. Especially, since GAN shows high quality in generating texture information of images, this paper adapts the GAN-based approach to eliminate blur noise. Typical GAN-based de-blurring methods [6] are processed by minimizing the difference of pixel values, which improves peak signal to noise ratio (PSNR). However, it cannot reflect the similarity for target images. Therefore, the proposed method uses the conditional GAN [7], which minimizes the difference of image distributions. In detail, proposed method adapts Wasserstein generative adversarial network with gradient penalty (WGANGP), which is based on conditional GAN. The Wasserstein generative adversarial network (WGAN) was proposed to stabilize the training of GAN by minimizing the Wasserstein distance of joint distributions instead of whole probability distributions [8]. Additionally, to improve the performance of WGAN, the gradient penalty term was proposed [9], known as a weight clipping method, in order to limit the gradient weight in range, and applying this term to the WGA can prevent the gradient vanishing and exploding problem. By applying the content loss function, i.e., the procedure of extracting feature maps in CNN, to WGANGP, the improved de-blurring method was proposed to capture the perceptual information of images [10]. The content loss function is one of many perceptual loss functions and improves the similarity between the blurred and sharp images. Applying the content loss function to WGANGP can capture the perceptual information, such as color and spatial structure. However, it does not preserve the detailed edge information of image at the same time. To preserve more edge information of the sharp image, another perceptual loss function called style loss function [11] is introduced. The style loss function extracts multiple feature maps in CNN and calculates by covariance matrix to capture more perceptual information and detailed edge. As a result, the style loss function captures higher similarity than the content loss function, shown with the similarity map in Figure 2. The figure shows that the boundary information between object and background is preserved in (b) better than (a). In this paper, the content loss function is replaced by the style loss function to preserve edge information and perceptual similarity. Substituting the style loss function to WGANGP can preserve the detailed edge since it can capture a large perceptual style and high level features. The objective function of network is proposed by combining the loss function of WGANGP and the style loss function. Therefore, the network generates de-blurred image while capturing the similarity of perceptual style and preserving detailed edge information. Figure 2: Similarity maps of (**a**) the content loss function and (**b**) the style loss function. In this work, it is experimentally shown that WGANGP with the style loss function can reconstruct edge-preserved de-blurring image and achieves high performance in structural similarity measure (SSIM). Also, a comparison experiment with different parameter values and the different locations of the feature map is performed to find the optimal solution.