  This paper tackles the problem of motion deblurring of dynamic scenes.
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 increasing the number of generic convolution layers and kernel-size,
but this comes at the expense of 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 across different spatial
locations and process each test image adaptively. We also propose an effective
content-aware global-local filtering module that significantly improves
performance by considering not only global dependencies but also by dynamically
exploiting neighbouring pixel information. We use a patch-hierarchical
attentive architecture composed of the above module that implicitly discovers
the spatial variations in the blur present in the input image and in turn,
performs local and global modulation of intermediate features. Extensive
qualitative and quantitative comparisons with prior art on deblurring
benchmarks demonstrate that our design offers significant improvements over the
state-of-the-art in accuracy as well as speed.
