Wednesday, March 08, 2006

bayesian super-resolution

Currently i am working on implementing a super-resolution algorithm which is based on bayesian image priors. This can be found in Capel's thesis on super-resolution. This method uses a generative model. The equations and ideas are almost identical to those in my previous post "failed start". Fortunately in capel's work he goes into grusome detail on many of the things which could not be understood in the other paper.

The generative model relies on the parameters that are found by the image registration algorithms, as well as some Bayesian image priors. Using these things and optimization it is then possible to solve for the super-resolution image.

4 Comments:

Blogger hemanth said...

Hi Jesse
I went through your blog on Bayesian Super resolution. Can you correspond with me since I am also doing a project on Super resolution. I am implementing from Capel's dissertation. I think I will have to go through all that you are doing in SR.
I am unable to get your id.
My id is
hemanthdv@rediffmail.com
hemanthdv@gmail.com
Do correspond and tell wether you are interested or not.

12:57 AM  
Blogger Ricardo said...

This may also be of interest:


R. O. Lane, Bayesian super-resolution with application to radar target recognition, Eng.D. thesis, Department of Electronic and Electrical Engineering, University College London, February 2008.

7:32 AM  
Blogger sneha said...

hey,i'm doing my project on super resolution.can u tell me some multi frame algo which can be done in 4-5 days.plz reply

12:04 AM  
Blogger Stefan van der Walt said...

I've written an open-source super-resolution library in Python, available here:

http://mentat.za.net

8:34 AM  

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