Our approach based on enhancing the resolution of video (data set of image which captured) through the addition of perceptually plausible high frequency information. By introducing an appropriate prior distribution over such data set we can ensure consistency of static image regions across successive frames of the video, and also take account of object motion. A key concept is the use of the previously enhanced frame to provide part of the training set for super-resolution enhancement of the current frame and that by adopted the local variance of reference frame as the foundation to establish the operator for projection to convex set POCS) and local threshold value for pixel-repair. And the results show that an improvement in video quality can be achieved.
[1]
Bir Bhanu,et al.
Human Recognition at a Distance in Video
,
2010,
Advances in Pattern Recognition.
[2]
S. Chaudhuri,et al.
Motion-Free Super-Resolution
,
2005
.
[3]
S. Chaudhuri.
Super-Resolution Imaging
,
2001
.
[4]
Moon Gi Kang,et al.
Super-resolution image reconstruction
,
2010,
2010 International Conference on Computer Application and System Modeling (ICCASM 2010).
[5]
Aggelos K. Katsaggelos,et al.
Super Resolution of Images and Video
,
2006,
Super Resolution of Images and Video.