IEEE Trans Image Process - Depth from motion and optical blur with an unscented Kalman filter.

Tópicos

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Resumo

Space-variantly blurred images of a scene contain valuable depth information. In this paper, our objective is to recover the 3-D structure of a scene from motion blur/optical defocus. In the proposed approach, the difference of blur between two observations is used as a cue for recovering depth, within a recursive state estimation framework. For motion blur, we use an unblurred-blurred image pair. Since the relationship between the observation and the scale factor of the point spread function associated with the depth at a point is nonlinear, we propose and develop a formulation of unscented Kalman filter for depth estimation. There are no restrictions on the shape of the blur kernel. Furthermore, within the same formulation, we address a special and challenging scenario of depth from defocus with translational jitter. The effectiveness of our approach is evaluated on synthetic as well as real data, and its performance is also compared with contemporary techniques.

Resumo Limpo

spacevari blur imag scene contain valuabl depth inform paper object recov d structur scene motion bluropt defocus propos approach differ blur two observ use cue recov depth within recurs state estim framework motion blur use unblurredblur imag pair sinc relationship observ scale factor point spread function associ depth point nonlinear propos develop formul unscent kalman filter depth estim restrict shape blur kernel furthermor within formul address special challeng scenario depth defocus translat jitter effect approach evalu synthet well real data perform also compar contemporari techniqu

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