  Removing spatially variant motion blur from a blurry image is a challenging
problem as blur sources are complicated and difficult to model accurately.
Recent progress in deep neural networks suggests that kernel free single image
deblurring can be efficiently performed, but questions about deblurring
performance persist. Thus, we propose to restore a sharp image by fusing a pair
of noisy/blurry images captured in a burst. Two neural network structures,
DeblurRNN and DeblurMerger, are presented to exploit the pair of images in a
sequential manner or parallel manner. To boost the training, gradient loss,
adversarial loss and spectral normalization are leveraged. The training dataset
that consists of pairs of noisy/blurry images and the corresponding ground
truth sharp image is synthesized based on the benchmark dataset GOPRO. We
evaluated the trained networks on a variety of synthetic datasets and real
image pairs. The results demonstrate that the proposed approach outperforms the
state-of-the-art both qualitatively and quantitatively.
