| Title |
New U-Net for Image Deblurring Using Deep Learning |
| Authors |
정우열(Wooyeol Jeong) ; 김성주(Sungjoo Kim) ; 이창우(Changwoo Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.7.843 |
| Keywords |
Image deblurring; Blind deblurring; U-Net; Motion blur; Gaussian blur |
| Abstract |
Many studies have been conducted for image deblurring, which is classified into non-blind and blind image deblurring techniques.
Many iterative methods have been studied based on the maximum-a-posteriori (MAP) framework for image deblurring. Recently, deep learning methods for blind image deblurring have attracted a lot of attention for their excellent performance. In this paper, a method for improving the performance of the blind image deblurring using deep learning is proposed by introducing a new structure of U-Net. U-Net is used as a deep neural network for deep learning in various image processing fields. We propose a new U-Net by using short cut and parallel structure in each stage of contractive and expansive path for U-Net, and pre-processing and post-processing are used for the proposed new U-Net to improve the deblurring performance. Extensive computer simulations are performed to evaluate the image deblurring performance for motion blur and Gaussian blur, and it is shown that the proposed U-Net shows superior image deblurring performance compared to the conventional U-Net. |