  The goal of blind image deblurring is to recover sharp image from one input
blurred image with an unknown blur kernel. Most of image deblurring approaches
focus on developing image priors, however, there is not enough attention to the
influence of image details and structures on the blur kernel estimation. What
is the useful image structure and how to choose a good deblurring region? In
this work, we propose a deep neural network model method for selecting good
regions to estimate blur kernel. First we construct image patches with labels
and train a deep neural networks, then the learned model is applied to
determine which region of the image is most suitable to deblur. Experimental
results illustrate that the proposed approach is effective, and could be able
to select good regions for image deblurring.
