This paper addresses the problem of joint image reconstruction and point spread function PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Simulation results demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo MCMC) version of myopic sparse reconstruction. It also outperforms non-myopic algorithms that rely on perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy MRFM).

Date Published: 10 February 2012
PDF: 11 pages
Proc. SPIE 8296, Computational Imaging X, 82960G (10 February 2012); doi: 10.1117/12.923764

Published in SPIE Proceedings Vol. 8296:
Computational Imaging X
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)