  The non-uniform blur of atmospheric turbulence can be modeled as a
superposition of linear motion blur kernels at a patch level. We propose a
regression convolutional neural network (CNN) to predict angle and length of a
linear motion blur kernel for varying sized patches. We analyze the robustness
of the network for different patch sizes and the performance of the network in
regions where the characteristics of the blur are transitioning. Alternating
patch sizes per epoch in training, we find coefficient of determination scores
across a range of patch sizes of $R^2>0.78$ for length and $R^2>0.94$ for angle
prediction. We find that blur predictions in regions overlapping two blur
characteristics transition between the two characteristics as overlap changes.
These results validate the use of such a network for prediction of non-uniform
blur characteristics at a patch level.
