Learning deep neural networks in blind deblurring framework
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Recently, end-to-end learning methods based on deep neural network (DNN) have been proven effective for blind deblurring. Without human-made assumptions and numerical algorithms, they are able to restore blurry images with fewer artifacts and better perceptual quality. However, without the theoretical guidance, these methods sometimes generate unreasonable results and often perform worse when the motion is complex. In this paper, for overcoming these drawbacks, we integrate deep convolution neural networks into conventional deblurring framework. Specifically, we build Stacked Estimate Residual Net (SEN) to estimate the motion flow map and Recurrent Prior Generative and Adversarial Net (RP-GAN) to learn an image prior constrained term in half-quadratic splitting algorithm. The generator and discriminators are also designed to be adaptive to the iterative optimization. Comparing with state-of-the-art end-to-end learning based methods, our method restores reasonable details and shows better generalization ability.
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