| 摘要 |
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), applicants 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, applicants show that a parabolic integration (corresponding to constant sensor acceleration) leads to motion blur that is not only invariant to object velocity, but preserves image frequency content nearly optimally. That is, static objects are degraded relative to their image from a static camera, but all moving objects within a given range of motions reconstruct well. 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.
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