  Many deblurring and blur kernel estimation methods use a maximum a posteriori
(MAP) approach or deep learning-based classification techniques to sharpen an
image and/or predict the blur kernel. We propose a regression approach using
convolutional neural networks (CNNs) to predict parameters of linear motion
blur kernels, the length and orientation of the blur. We analyze the
relationship between length and angle of linear motion blur that can be
represented as digital filter kernels. A large dataset of blurred images is
generated using a suite of blur kernels and used to train a regression CNN for
prediction of length and angle of the motion blur. The coefficients of
determination for estimation of length and angle are found to be greater than
or equal to 0.89, even under the presence of significant additive Gaussian
noise, up to a variance of 10\% (SNR of 10 dB). Using our estimated kernel in a
non-blind image deblurring method, the sum of squared differences error ratio
demonstrates higher cumulative histogram values than comparison methods, with
most test images yielding an error ratio of less than or equal to 1.25.
