  In this paper, we address the problem of dynamic scene deblurring in the
presence of motion blur. Restoration of images affected by severe blur
necessitates a network design with a large receptive field, which existing
networks attempt to achieve through simple increment in the number of generic
convolution layers, kernel-size, or the scales at which the image is processed.
However, increasing the network capacity in this manner comes at the expense of
increase in model size and inference speed, and ignoring the non-uniform nature
of blur. We present a new architecture composed of spatially adaptive residual
learning modules that implicitly discover the spatially varying shifts
responsible for non-uniform blur in the input image and learn to modulate the
filters. This capability is complemented by a self-attentive module which
captures non-local relationships among the intermediate features and enhances
the receptive field. We then incorporate a spatiotemporal recurrent module in
the design to also facilitate efficient video deblurring. Our networks can
implicitly model the spatially-varying deblurring process, while dispensing
with multi-scale processing and large filters entirely. Extensive qualitative
and quantitative comparisons with prior art on benchmark dynamic scene
deblurring datasets clearly demonstrate the superiority of the proposed
networks via reduction in model-size and significant improvements in accuracy
and speed, enabling almost real-time deblurring.
