Sharp Processing of Blur Image Based on Generative Adversarial Network

摘要

Aiming at the very challenging problem of motion blur caused by camera shake, object movement, etc. the traditional method using blur kernel estimation easily leads to estimation errors and makes the image restoration effect poor. We propose a deep convolutional neural network solution to restore blurred images. It is based on DeblurGAN to directly obtain deblurred images from end-to-end motion blurred images. and improves the residual network to effectively restore the detailed information of the image, Finally, through the training and testing of the deep convolution neural network model, it is proved that the method can achieve state-of-the-art performance in several commonly used indexes.