Physics – Data Analysis – Statistics and Probability
Scientific paper
2012-03-21
Physics
Data Analysis, Statistics and Probability
This work has been submitted to the IEEE Trans. Image Processing for possible publication
Scientific paper
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization (AM) algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.
Dobigeon Nicolas
Hero Alfred O.
Park Sung Un
No associations
LandOfFree
Semi-blind Sparse Image Reconstruction with Application to MRFM does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Semi-blind Sparse Image Reconstruction with Application to MRFM, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Semi-blind Sparse Image Reconstruction with Application to MRFM will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-488617