  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. Most existing approaches achieve a large
receptive field by increasing the number of generic convolution layers and
kernel size. In this work, we propose a pixel adaptive and feature attentive
design for handling large blur variations across different spatial locations
and process each test image adaptively. We design a content-aware global-local
filtering module that significantly improves performance by considering not
only global dependencies but also by dynamically exploiting neighboring pixel
information. We further introduce a pixel-adaptive non-uniform sampling
strategy that implicitly discovers the difficult-to-restore regions present in
the image and, in turn, performs fine-grained refinement in a progressive
manner. Extensive qualitative and quantitative comparisons with prior art on
deblurring benchmarks demonstrate that our approach performs favorably against
the state-of-the-art deblurring algorithms.
