Blind Image Deblurring based on Deep Image Prior

Many studies on image deblurring have been conducted, and deep learning methods for blind image deblurring have received considerable attention due to their good performance. Recently, the SelfDeblur method was proposed for blind image deblurring based on deep image prior (DIP). In the SelfDeblur method, two neural networks for an image generator and a blur kernel generator are learned simultaneously with only one blurry image. This shows the feasibility of blind image deblurring using unsupervised learning, since it requires no training process. In this paper, we propose a method to maximize the performance of blind image deblurring based on DIP. The optimal loss function for deep learning is studied for the SelfDeblur method, and the deblurring performance of the proposed method is stabilized and maximized using the image prior and the kernel prior for the total loss function. Extensive computer simulations show that the proposed method yields superior performance compared to conventional methods. © 2022 Institute of Electronics and Information Engineers. All rights reserved.