Motion-invariant photography
Abstract
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 degraded relative to their image from a static camera, a reliable reconstruction of all moving objects within a given velocities range is made possible. We have built a prototype camera and present successful deblurring results over a wide variety of human motions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2008
- Source ID
- 10.1145/1360612.1360670
Entities
People
- Anat Levin
- Frédo Durand
- Peter Sand
- Taeg Sang Cho
- William T. Freeman
Organizations
- MIT Computer Science and Artificial Intelligence Laboratory
- National Geospatial-Intelligence Agency
- National Science Foundation
- Office of Naval Research