Abstract
Due to the spatially variant blur caused by camera shake
and object motions under different scene depths, deblurring
images captured from dynamic scenes is challenging. Although recent works based on deep neural networks have
shown great progress on this problem, their models are usually large and computationally expensive. In this paper, we
propose a novel spatially variant neural network to address
the problem. The proposed network is composed of three
deep convolutional neural networks (CNNs) and a recurrent
neural network (RNN). RNN is used as a deconvolution operator performed on feature maps extracted from the input
image by one of the CNNs. Another CNN is used to learn the
weights for the RNN at every location. As a result, the RNN
is spatially variant and could implicitly model the deblurring process with spatially variant kernels. The third CNN
is used to reconstruct the final deblurred feature maps into
restored image. The whole network is end-to-end trainable.
Our analysis shows that the proposed network has a large
receptive field even with a small model size. Quantitative
and qualitative evaluations on public datasets demonstrate
that the proposed method performs favorably against stateof-the-art algorithms in terms of accuracy, speed, and model
size