  This paper tackles the problem of dynamic scene deblurring. Although
end-to-end fully convolutional designs have recently advanced the
state-of-the-art in non-uniform motion deblurring, their performance-complexity
trade-off is still sub-optimal. Existing approaches achieve a large receptive
field by a simple increment in the number of generic convolution layers,
kernel-size, which comes with the burden of the increase in model size and
inference speed. In this work, we propose an efficient pixel adaptive and
feature attentive design for handling large blur variations within and across
different images. We also propose an effective content-aware global-local
filtering module that significantly improves the performance by considering not
only the global dependencies of the pixel but also dynamically using the
neighboring pixels. We use a patch hierarchical attentive architecture composed
of the above module that implicitly discover the spatial variations in the blur
present in the input image and in turn perform local and global modulation of
intermediate features. Extensive qualitative and quantitative comparisons with
prior art on deblurring benchmarks demonstrate the superiority of the proposed
network.
