Single Image Blind Deblurring with Deep Recursive Networks

Abstract

Single image deblurring aims to retore the latent sharp images from the corresponding blurred ones, which is a highly ill-posed problem. In this paper, we present a deep recursive network based on generatetive adversarial networks (GANs) that restores sharp images in an end-to-end manner where blur is caused by various sources, our recursive neural network can greatly reduce the computation and complexity of the model. Then we use the least square discriminator to prevent the gradient from disappearing and make the training process more stable. We also add an adversarial loss to make the generated images look more realistic and a perceptual loss to generated better image. Experimental results have shown that our proposed method produces better performance and faster time.