TITLE:
New Approach for Limited-Angle Problems in Electron Microscope Based on Compressed Sensing
AUTHORS:
Marc Vila Oliva, Hamed Hamid Muhammed
KEYWORDS:
Compressed Sensing; Image Reconstruction; Adaptive Filters; Limited Angle Problem; TEM
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
February
14,
2014
ABSTRACT: New advances within the recently rediscovered field of
Compressed Sensing (CS) have opened for a great variety of new possibilities in
the field of image reconstruction and more specifically in medical
image reconstruction. In this work, a new approach using a CS-based algorithm
is proposed and used in order to solve limited-angle problems (LAPs), like the
ones that typically occur in computed tomography or electron microscope. This
approach is based on a variant of the Robbins-Monro stochastic approximation
procedure, developed by Egaziarian, using regularization by a spatially
adaptive filter. This proposal consists on filling the gaps of
missing or unobserved data with random noise and enabling a spatially adaptive
denoising filter to regularize the data and reveal the underlying topology. This
method was tested on different 3D transmission electron microscope datasets
that presented different missing data artifacts (e.g, wedge or cone shape). The
test results show a great potential for solving LAPs using the proposed
technique.