Abstract: Existing deep learning based image deblurring methods are weak in preserving the high frequency texture details.They may also be time-consuming and cause severe ringing effects in the restored result.This paper proposes a novel approach based on the convolutional neural networks（CNN） to overcome these limits.A CNN model called fast CNN（FCNN） is introduced to deblur the image quickly while keeping the high frequency details.With FCNN,the method first deblurs the image in the Fourier domain with regularizations to obtain a pre-processed latent image.Then,the block of the pre-processed results and its corresponding clean blocks are served as input and label to train the FCNN,so that the mapping function from blurring image to potential clean image is obtained.Consequently,a clear image can be obtained by the trained FCNN.Qualitative and quantitative experiments demonstrate that the proposed method adopts the parameter-sharing property of CNN effectively and reduces the number of training parameters significantly.It also greatly reduces the computational complexity in comparison with the existing deep learning based algorithms while keeps the image details well.