  We present a neural network model approach for multi-frame blind
deconvolution. The discriminative approach adopts and combines two recent
techniques for image deblurring into a single neural network architecture. Our
proposed hybrid-architecture combines the explicit prediction of a
deconvolution filter and non-trivial averaging of Fourier coefficients in the
frequency domain. In order to make full use of the information contained in all
images in one burst, the proposed network embeds smaller networks, which
explicitly allow the model to transfer information between images in early
layers. Our system is trained end-to-end using standard backpropagation on a
set of artificially generated training examples, enabling competitive
performance in multi-frame blind deconvolution, both with respect to quality
and runtime.
