Robust regression methods for computer vision: a review
International Journal of Computer Vision
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
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
ACM SIGGRAPH 2005 Papers
MM '08 Proceedings of the 16th ACM international conference on Multimedia
ACM Transactions on Graphics (TOG)
Spatio-temporal Super-Resolution Using Depth Map
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Resolution enhancement based on learning the sparse association of image patches
Pattern Recognition Letters
Resolution enhancement by vibrating displays
ACM Transactions on Graphics (TOG)
Apparent resolution enhancement for motion videos
Proceedings of the ACM Symposium on Applied Perception
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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 "jitter camera," 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.