Oleg Michailovich
Spatially regularized compressed sensing of diffusion MRI data.
Mon, 09/13/2010 - 15:01 — SomNambulAuthors: Oleg Michailovich, Yogesh Rathi
Subjects: Information Theory
AbstractThe present paper introduces a method for substantial reduction of the number
of diffusion encoding gradients required for reliable reconstruction of HARDI
signals. The method exploits the theory of compressed sensing (CS), which
establishes conditions on which a signal of interest can be recovered from its
under-sampled measurements, provided that the signal admits a sparse
representation in the domain of a linear transform. In the case at hand, the
latter is defined to be spherical ridgelet transformation, which excels in
sparsifying HARDI signals.Image Segmentation Using Weak Shape Priors.
Tue, 06/15/2010 - 15:01 — SomNambulAuthors: Oleg Michailovich, Robert Sheng Xu, Magdy Salama
Subjects: Computer Vision and Pattern Recognition
AbstractThe problem of image segmentation is known to become particularly challenging
in the case of partial occlusion of the object(s) of interest, background
clutter, and the presence of strong noise. To overcome this problem, the
present paper introduces a novel approach segmentation through the use of
"weak" shape priors.Regularized Richardson-Lucy Algorithm for Sparse Reconstruction of Poissonian Images.
Fri, 04/09/2010 - 15:01 — SomNambulAuthors: Oleg Michailovich, Elad Shaked
Subjects: Computer Vision and Pattern Recognition
AbstractRestoration of digital images from their degraded measurements has always
been a problem of great theoretical and practical importance in numerous
applications of imaging sciences. A specific solution to the problem of image
restoration is generally determined by the nature of degradation phenomenon as
well as by the statistical properties of measurement noises. The present study
is concerned with the case in which the images of interest are corrupted by
convolutional blurs and Poisson noises.An Iterative Shrinkage Approach to Total-Variation Image Restoration.
Wed, 10/28/2009 - 16:01 — SomNambulAuthors: Oleg Michailovich
Subjects: Computer Vision and Pattern Recognition
AbstractThe problem of restoration of digital images from their degraded measurements
plays a central role in a multitude of practically important applications. A
particularly challenging instance of this problem occurs in the case when the
degradation phenomenon is modeled by an ill-conditioned operator. In such a
case, the presence of noise makes it impossible to recover a valuable
approximation of the image of interest without using some a priori information
about its properties. Such a priori information is essential for image
restoration, rendering it stable and robust to noise.
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