Non‐uniform image blind deblurring by two‐stage fully convolution network

Abstract

Deep neural networks have recently demonstrated high performance for deblurring. However, few methods are designed for both non‐uniform image blur estimation and removal with highly efficient. In this study, the authors proposed a fully convolutional network that outputs estimated blur and restored image in one feed‐forward pass for the non‐uniformly blurred image of any input‐size. The proposed network contains two subnets. The parameter estimation subnet P‐net predicts pixel‐wise parameters of multiple blur types with high accuracy. The output of P‐net is used as a condition, which guides the blur removal subnet G‐net to restore a high quality latent sharp image. P‐net and G‐net are ultimately integrated into a single framework called PG‐net, which guarantees the consistency of parameter estimation and blur removal, thereby improves algorithm efficiency. Experiment results show that the authors blur parameter estimation method as well as their deblurring method outperforms the comparison methods both quantitatively and qualitatively.

Keywords

• nonuniform image blind deblurring
• two‐stage fully convolution network
• deep neural networks
• fully convolutional network
• blur restored image
• feed‐forward pass