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.

mosaicing

I now have a working mosaicing algorithm. Here are the pictures.






These are the starting sequence of images. I got them by cropping a set of images I received thanks to kristin branson, a computer science graduate student at ucsd.







These are the four original images after we have found the homography that maps each image into the coordinate system defined by the fourth image.




Finaly, this is the composite mosaic. Currently this is done using a simple method of picking pixels for the final image based on poximity to the centers of the four transformed images. As can be seen this algorithm results in a blank spot in the the mosaic where clearly there are some overlapping images. There are several smarter ways to implement this, but since my focus is on super-resolution that will be lower on my priority list.