Efficient Marginal Likelihood Optimization in Blind Deconvolution
In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k|y) and not only its mode. This leads to a distinction between MAPx...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Published: |
2011
|
| Online Access: | http://hdl.handle.net/1721.1/62035 |