  We present a new method for blind motion deblurring that uses a neural
network trained to compute estimates of sharp image patches from observations
that are blurred by an unknown motion kernel. Instead of regressing directly to
patch intensities, this network learns to predict the complex Fourier
coefficients of a deconvolution filter to be applied to the input patch for
restoration. For inference, we apply the network independently to all
overlapping patches in the observed image, and average its outputs to form an
initial estimate of the sharp image. We then explicitly estimate a single
global blur kernel by relating this estimate to the observed image, and finally
perform non-blind deconvolution with this kernel. Our method exhibits accuracy
and robustness close to state-of-the-art iterative methods, while being much
faster when parallelized on GPU hardware.
