  Image motion blur usually results from moving objects or camera shakes. Such
blur is generally directional and non-uniform. Previous research efforts
attempt to solve non-uniform blur by using self-recurrent multi-scale or
multi-patch architectures accompanying with self-attention. However, using
self-recurrent frameworks typically leads to a longer inference time, while
inter-pixel or inter-channel self-attention may cause excessive memory usage.
This paper proposes blur-aware attention networks (BANet) that accomplish
accurate and efficient deblurring via a single forward pass. Our BANet utilizes
region-based self-attention with multi-kernel strip pooling to disentangle blur
patterns of different degrees and with cascaded parallel dilated convolution to
aggregate multi-scale content features. Extensive experimental results on the
GoPro and HIDE benchmarks demonstrate that the proposed BANet performs
favorably against the state-of-the-art in blurred image restoration and can
provide deblurred results in realtime.
