Robust regression methods for computer vision: a review
International Journal of Computer Vision
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
A note on the use of determinant for proving lower bounds on the size of linear circuits
Information Processing Letters
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer 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
Increasing Space-Time Resolution in Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Super-Resolution Enhancement of Text Image Sequences
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Full-Frame Video Stabilization with Motion Inpainting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video super-resolution by integrating SAD and NCC matching criterion for multiple moving objects
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
Super-resolution image with estimated high frequency compensated algorithm
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Depth reconstruction uncertainty analysis and improvement - The dithering approach
Image and Vision Computing
Flexible voxels for motion-aware videography
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multiframe super-resolution reconstruction of small moving objects
IEEE Transactions on Image Processing
Local object-based super-resolution mosaicing from low-resolution video
Signal Processing
Resolution-enhanced photometric stereo
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
Performance Capture of High-Speed Motion Using Staggered Multi-View Recording
Computer Graphics Forum
On Plenoptic Multiplexing and Reconstruction
International Journal of Computer Vision
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Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. The resolution of videos can be computationally enhanced by moving the camera and applying super-resolution reconstruction algorithms. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super-resolution. The conclusion is that, in order to achieve the highest resolution, motion blur should be avoided. Motion blur can be minimized by sampling the space-time volume of the video in a specific manner. We have developed a novel camera, called the 驴jitter camera,驴 that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.