Robust Parameter Estimation in Computer Vision
SIAM Review
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
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
ACM SIGGRAPH 2008 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
The Frankencamera: an experimental platform for computational photography
ACM SIGGRAPH 2010 papers
Anti-blur feedback for visually impaired users of smartphone cameras
Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
Single image deblurring using motion density functions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Sensor synaesthesia: touch in motion, and motion in touch
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PSO based motion deblurring for single image
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Non-uniform motion deblurring for camera shakes using image registration
ACM SIGGRAPH 2011 Talks
Intelligent Service Robotics
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
Non-uniform Deblurring for Shaken Images
International Journal of Computer Vision
High-quality image deblurring with panchromatic pixels
ACM Transactions on Graphics (TOG)
Local phase quantization for blur-insensitive image analysis
Image and Vision Computing
GripSense: using built-in sensors to detect hand posture and pressure on commodity mobile phones
Proceedings of the 25th annual ACM symposium on User interface software and technology
Performance Capture of High-Speed Motion Using Staggered Multi-View Recording
Computer Graphics Forum
Registration Based Non-uniform Motion Deblurring
Computer Graphics Forum
Blur-Kernel estimation from spectral irregularities
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Near-invariant blur for depth and 2D motion via time-varying light field analysis
ACM Transactions on Graphics (TOG)
Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure
Advanced Engineering Informatics
Patch mosaic for fast motion deblurring
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Single-Image blind deblurring for non-uniform camera-shake blur
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
MRF-Based blind image deconvolution
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Kernel estimation from salient structure for robust motion deblurring
Image Communication
Improved image deblurring based on salient-region segmentation
Image Communication
Robust blind motion deblurring using near-infrared flash image
Journal of Visual Communication and Image Representation
Shape from Sharp and Motion-Blurred Image Pair
International Journal of Computer Vision
Hi-index | 0.00 |
We present a deblurring algorithm that uses a hardware attachment coupled with a natural image prior to deblur images from consumer cameras. Our approach uses a combination of inexpensive gyroscopes and accelerometers in an energy optimization framework to estimate a blur function from the camera's acceleration and angular velocity during an exposure. We solve for the camera motion at a high sampling rate during an exposure and infer the latent image using a joint optimization. Our method is completely automatic, handles per-pixel, spatially-varying blur, and out-performs the current leading image-based methods. Our experiments show that it handles large kernels -- up to at least 100 pixels, with a typical size of 30 pixels. We also present a method to perform "ground-truth" measurements of camera motion blur. We use this method to validate our hardware and deconvolution approach. To the best of our knowledge, this is the first work that uses 6 DOF inertial sensors for dense, per-pixel spatially-varying image deblurring and the first work to gather dense ground-truth measurements for camera-shake blur.