Dynamically reparameterized light fields
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Robot Vision
Motion Deblurring and Super-resolution from an Image Sequence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
A Slit Scanning Depth of Route Panorama from Stationary Blur
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Frequency analysis and sheared reconstruction for rendering motion blur
ACM SIGGRAPH 2009 papers
Invertible motion blur in video
ACM SIGGRAPH 2009 papers
4D frequency analysis of computational cameras for depth of field extension
ACM SIGGRAPH 2009 papers
ACM SIGGRAPH Asia 2009 papers
Image deblurring using inertial measurement sensors
ACM SIGGRAPH 2010 papers
Technical Section: Real-time temporal shaping of high-speed video streams
Computers and Graphics
Single image deblurring using motion density functions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Velocity-dependent shutter sequences for motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Video deblurring and super-resolution technique for multiple moving objects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
Blind image deblurring with modified richardson-lucy deconvolution for ringing artifact suppression
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Edge-guided resolution enhancement in projectors via optical pixel sharing
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
High-quality image deblurring with panchromatic pixels
ACM Transactions on Graphics (TOG)
Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
Performance Capture of High-Speed Motion Using Staggered Multi-View Recording
Computer Graphics Forum
Blur-Kernel estimation from spectral irregularities
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Image enhancement using calibrated lens simulations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Near-invariant blur for depth and 2D motion via time-varying light field analysis
ACM Transactions on Graphics (TOG)
Motion-Invariant coding using a programmable aperture camera
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
5D Covariance tracing for efficient defocus and motion blur
ACM Transactions on Graphics (TOG)
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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.