딥 러닝을 이용한 영상 디블러링을 위한 새로운 U-Net = New U-Net for Image Deblurring Using Deep Learning

다국어 초록 (Multilingual 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.