Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video-to-video dynamic super-resolution for grayscale and color sequences
EURASIP Journal on Applied Signal Processing
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
New adaptive pixel decimation for block motion vector estimation
IEEE Transactions on Circuits and Systems for Video Technology
Spatial-temporal motion compensation based video super resolution
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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A typical dynamic reconstruction-based super-resolution video involves three independent processes: registration, fusion and restoration. Fast video super-resolution systems apply translational motion compensation model for registration with low computational cost. Traditional motion compensation model assumes that the whole spectrum of pixels is consistent between frames. In reality, the low frequency component of pixels often varies significantly. We propose a translational motion compensation model via frequency classification for video super-resolution systems. A novel idea to implement motion compensation by combining the up-sampled current frame and the high frequency part of the previous frame through the SAD framework is presented. Experimental results show that the new motion compensation model via frequency classification has an advantage of 2dB gain on average over that of the traditional motion compensation model. The SR quality has 0.25dB gain on average after the fusion process which is to minimize error by making use of the new motion compensated frame.