  We present a deblurring method for scenes with occluding objects using a
carefully designed layered blur model. Layered blur model is frequently used in
the motion deblurring problem to handle locally varying blurs, which is caused
by object motions or depth variations in a scene. However, conventional models
have a limitation in representing the layer interactions occurring at occlusion
boundaries. In this paper, we address this limitation in both theoretical and
experimental ways, and propose a new layered blur model reflecting actual blur
generation process. Based on this model, we develop an occlusion-aware
deblurring method that can estimate not only the clear foreground and
background, but also the object motion more accurately. We also provide a novel
analysis on the blur kernel at object boundaries, which shows the distinctive
characteristics of the blur kernel that cannot be captured by conventional blur
models. Experimental results on synthetic and real blurred videos demonstrate
that the proposed method yields superior results, especially at object
boundaries.
