  Blur detection is the separation of blurred and clear regions of an image,
which is an important and challenging task in computer vision. In this work, we
regard blur detection as an image segmentation problem. Inspired by the success
of the U-net architecture for image segmentation, we design a Multi-Scale
Dilated convolutional neural network based on U-net, which we call MSDU-net.
The MSDU-net uses a group of multi-scale feature extractors with dilated
convolutions to extract texture information at different scales. The U-shape
architecture of the MSDU-net fuses the different-scale texture features and
generates a semantic feature which allows us to achieve better results on the
blur detection task. We show that using the MSDU-net we are able to outperform
other state of the art blur detection methods on two publicly available
benchmarks.
