We address the problem of camera motion estimation from a single blurred image with the aid of deep convolutional neural networks.
Unlike learning-based prior works that estimate a space-invariant blur kernel, we solve for the global camera motion which in turn
represents the space-variant blur at each pixel. Leveraging the camera motion as well as the clean reference image during training, we resort to a semi-supervised training scheme that utilizes the strengths of both supervised and unsupervised learning to solve for the camera motion undergone by a space-variant blurred image. Finally, we show the effectiveness of such a motion estimation network with applications in space-variant deblurring and change detection.