Abstract
Deblurring from a motion blurred image has been studied for some times. After convolution neural network(CNN) be used widely, it can be implemented on finding blur kernel or latent sharp edge from a blurred image. In recent years, the most popular network architecture called generative adversarial network (GAN) which performs well on style transformation. We consider that a deblurring problem as a style transformation problem, so we use GAN to do deblurring. We focus on improving the state-of-the-art deblur method DeblurGAN’s generator, and present a new kind of block which combined inception block, residual block and dense block to do deblurring from motion blur. By using the conception of dense net which can avoid overfitting. The improved DeblurGAN present better in both structural similarity measure and by visual effect.