  Video deblurring is a challenging problem as the blur is complex and usually
caused by the combination of camera shakes, object motions, and depth
variations. Optical flow can be used for kernel estimation since it predicts
motion trajectories. However, the estimates are often inaccurate in complex
scenes at object boundaries, which are crucial in kernel estimation. In this
paper, we exploit semantic segmentation in each blurry frame to understand the
scene contents and use different motion models for image regions to guide
optical flow estimation. While existing pixel-wise blur models assume that the
blur kernel is the same as optical flow during the exposure time, this
assumption does not hold when the motion blur trajectory at a pixel is
different from the estimated linear optical flow. We analyze the relationship
between motion blur trajectory and optical flow, and present a novel pixel-wise
non-linear kernel model to account for motion blur. The proposed blur model is
based on the non-linear optical flow, which describes complex motion blur more
effectively. Extensive experiments on challenging blurry videos demonstrate the
proposed algorithm performs favorably against the state-of-the-art methods.
