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TOG
2008

Motion-invariant photography

8 years 11 months ago
Motion-invariant photography
Object motion during camera exposure often leads to noticeable blurring artifacts. Proper elimination of this blur is challenging because the blur kernel is unknown, varies over the image as a function of object velocity, and destroys high frequencies. In the case of motions along a 1D direction (e.g. horizontal) we show that these challenges can be addressed using a camera that moves during the exposure. Through the analysis of motion blur as space-time integration, we show that a parabolic integration (corresponding to constant sensor acceleration) leads to motion blur that is invariant to object velocity. Thus, a single deconvolution kernel can be used to remove blur and create sharp images of scenes with objects moving at different speeds, without requiring any segmentation and without knowledge of the object speeds. Apart from motion invariance, we prove that the derived parabolic motion preserves image frequency content nearly optimally. That is, while static objects are degrade...
Anat Levin, Peter Sand, Taeg Sang Cho, Fréd
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TOG
Authors Anat Levin, Peter Sand, Taeg Sang Cho, Frédo Durand, William T. Freeman
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