Integrating Neural Networks Into the Blind Deblurring Framework to Compete With the End-to-End Learning-Based Methods

Recently, the end-to-end learning-based methods have been proven effective for the blind image deblurring. Without human-made assumptions or numerical algorithms, they are able to restore images with fewer artifacts and better perceptual quality. However, in practice, these methods suffer from limited performance under complex motion scenario and produces unnatural results sometimes. In this paper, in order to overcome their limitations, we propose to integrate deep convolution neural networks into a conventional deblurring framework. Specifically, we propose Stacked Estimation Residual Net (SEN) to estimate the motion flow map and Recurrent Prior Generative and Adversarial Net (RP-GAN) to learn the implicit image prior for the optimization. Comparing with the state-of-the-art end-to-end learning-based methods, the proposed method restores image content more naturally and shows better generalization ability.