Abstarct: The use of Wireless Capsule Endoscopy (WCE) technology, which is only two decades old, has been growing in recent years. The capsule moves through the gastrointestinal tract and captures images. Specialists use these images to diagnose a variety of diseases. To correctly diagnose diseases, the resulting images need to have high quality and resolution. The blurry images cause the specialist not to diagnose or misdiagnose. The complex conditions for capturing images, low brightness, and limited equipment are among the reasons for their low quality and sharpness. This capsule moves due to slipping and rotational movements. This constant movement creates a blur in these images. This study aimed to deblur the images of WCE. Deblurring an image is a challenging task, as the type of blur must be identified and, also, the kernel of the blur estimated. In these images, three types of Gaussian, radial, and circular blur have been identified. In the proposed method, we first examine the blurriness using an appropriate metric. We use the unsharp masking method to deblur the images with Gaussian blur. This method amplifies the edges and the high-frequency areas of the images. The Wiener filter is another method that has been used to deblur all types of blur in the past. We used the Wiener filter method to remove radial and circular blur. To estimate the blur kernel, we used simulated images with radial and circular blur. In fact, according to the procedure of generating the simulated images and the amount of this type of blur in the WCE images, the kernel of blur can be estimated. We evaluate the results baxsed on the GIANA 2017 challenge dataset. The results show that the blurring rate is reduced by about 30% to 85%.