1,102 research outputs found
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a
low or even ultra-low level while maintaining the high quality of reconstructed
images. We propose a new method for CT reconstruction that combines penalized
weighted-least squares reconstruction (PWLS) with regularization based on a
sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images.
We adopt an alternating algorithm to optimize the PWLS-ST cost function that
alternates between a CT image update step and a sparse coding step. We adopt a
relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed
OS-LALM) to accelerate the CT image update step by reducing the number of
forward and backward projections. Numerical experiments on the XCAT phantom
show that for low dose levels, the proposed PWLS-ST method dramatically
improves the quality of reconstructed images compared to PWLS reconstruction
with a nonadaptive edge-preserving regularizer (PWLS-EP).Comment: This is a revised and corrected version of the IEEE IVMSP Workshop
paper DOI: 10.1109/IVMSPW.2016.752821
Neural Representations for Sensory-Motor Control, II: Learning a Head-Centered Visuomotor Representation of 3-D Target Position
A neural network model is described for how an invariant head-centered representation of 3-D target position can be autonomously learned by the brain in real time. Once learned, such a target representation may be used to control both eye and limb movements. The target representation is derived from the positions of both eyes in the head, and the locations which the target activates on the retinas of both eyes. A Vector Associative Map, or YAM, learns the many-to-one transformation from multiple combinations of eye-and-retinal position to invariant 3-D target position. Eye position is derived from outflow movement signals to the eye muscles. Two successive stages of opponent processing convert these corollary discharges into a. head-centered representation that closely approximates the azimuth, elevation, and vergence of the eyes' gaze position with respect to a cyclopean origin located between the eyes. YAM learning combines this cyclopean representation of present gaze position with binocular retinal information about target position into an invariant representation of 3-D target position with respect to the head. YAM learning can use a teaching vector that is externally derived from the positions of the eyes when they foveate the target. A YAM can also autonomously discover and learn the invariant representation, without an explicit teacher, by generating internal error signals from environmental fluctuations in which these invariant properties are implicit. YAM error signals are computed by Difference Vectors, or DVs, that are zeroed by the YAM learning process. YAMs may be organized into YAM Cascades for learning and performing both sensory-to-spatial maps and spatial-to-motor maps. These multiple uses clarify why DV-type properties are computed by cells in the parietal, frontal, and motor cortices of many mammals. YAMs are modulated by gating signals that express different aspects of the will-to-act. These signals transform a single invariant representation into movements of different speed (GO signal) and size (GRO signal), and thereby enable YAM controllers to match a planned action sequence to variable environmental conditions.National Science Foundation (IRI-87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-1309
First order algorithms in variational image processing
Variational methods in imaging are nowadays developing towards a quite
universal and flexible tool, allowing for highly successful approaches on tasks
like denoising, deblurring, inpainting, segmentation, super-resolution,
disparity, and optical flow estimation. The overall structure of such
approaches is of the form ; where the functional is a data fidelity term also
depending on some input data and measuring the deviation of from such
and is a regularization functional. Moreover is a (often linear)
forward operator modeling the dependence of data on an underlying image, and
is a positive regularization parameter. While is often
smooth and (strictly) convex, the current practice almost exclusively uses
nonsmooth regularization functionals. The majority of successful techniques is
using nonsmooth and convex functionals like the total variation and
generalizations thereof or -norms of coefficients arising from scalar
products with some frame system. The efficient solution of such variational
problems in imaging demands for appropriate algorithms. Taking into account the
specific structure as a sum of two very different terms to be minimized,
splitting algorithms are a quite canonical choice. Consequently this field has
revived the interest in techniques like operator splittings or augmented
Lagrangians. Here we shall provide an overview of methods currently developed
and recent results as well as some computational studies providing a comparison
of different methods and also illustrating their success in applications.Comment: 60 pages, 33 figure
A Bayesian approach to the aperture problem of 3D motion perception
We suggest a geometric-statistical approach that can be ap-
plied to the 3D aperture problem of motion perception. In
simulations and psychophysical experiments we study per-
ceived 3D motion direction in a binocular viewing geometry
by systematically varying 3D orientation of a line stimulus
moving behind a circular aperture. Although motion direc-
tion is inherently ambiguous perceived directions show sys-
tematic trends and a Bayesian model with a prior for small
depth followed by slow motion in 3D gives reasonable fits to
individual data. We conclude that the visual system tries to minimize velocity in 3D but that earlier disparity processing strongly influences perceived 3D motion direction. We discuss implications for the integration of disparity and motion cues in the human visual system
Vision during manned booster operation Final report
Retinal images and accomodation control mechanism under conditions of space flight stres
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