Estimation of motion blur kernel parameters using regression convolutional neural networks

Many deblurring and blur kernel estimation methods use MAP or classification deep learning techniques to sharpen an image and predict the blur kernel. We propose a regression approach using neural networks to predict the parameters of linear motion blur kernels. These kernels can be parameterized by its length of blur and the orientation of the blur.This paper will analyze the relationship between length and angle of linear motion blur. This analysis will help establish a foundation to using regression prediction in uniformed motion blur images.