  The human visual system excels at detecting local blur of visual images, but
the underlying mechanism is not well understood. Traditional views of blur such
as reduction in energy at high frequencies and loss of phase coherence at
localized features have fundamental limitations. For example, they cannot well
discriminate flat regions from blurred ones. Here we propose that high-level
semantic information is critical in successfully identifying local blur.
Therefore, we resort to deep neural networks that are proficient at learning
high-level features and propose the first end-to-end local blur mapping
algorithm based on a fully convolutional network. By analyzing various
architectures with different depths and design philosophies, we empirically
show that high-level features of deeper layers play a more important role than
low-level features of shallower layers in resolving challenging ambiguities for
this task. We test the proposed method on a standard blur detection benchmark
and demonstrate that it significantly advances the state-of-the-art (ODS
F-score of 0.853). Furthermore, we explore the use of the generated blur maps
in three applications, including blur region segmentation, blur degree
estimation, and blur magnification.
