33,584 research outputs found
Exploiting spatial sparsity for multi-wavelength imaging in optical interferometry
Optical interferometers provide multiple wavelength measurements. In order to
fully exploit the spectral and spatial resolution of these instruments, new
algorithms for image reconstruction have to be developed. Early attempts to
deal with multi-chromatic interferometric data have consisted in recovering a
gray image of the object or independent monochromatic images in some spectral
bandwidths. The main challenge is now to recover the full 3-D (spatio-spectral)
brightness distribution of the astronomical target given all the available
data. We describe a new approach to implement multi-wavelength image
reconstruction in the case where the observed scene is a collection of
point-like sources. We show the gain in image quality (both spatially and
spectrally) achieved by globally taking into account all the data instead of
dealing with independent spectral slices. This is achieved thanks to a
regularization which favors spatial sparsity and spectral grouping of the
sources. Since the objective function is not differentiable, we had to develop
a specialized optimization algorithm which also accounts for non-negativity of
the brightness distribution.Comment: This version has been accepted for publication in J. Opt. Soc. Am.
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
This paper proposes a novel method to estimate the global scale of a 3D
reconstructed model within a Kalman filtering-based monocular SLAM algorithm.
Our Bayesian framework integrates height priors over the detected objects
belonging to a set of broad predefined classes, based on recent advances in
fast generic object detection. Each observation is produced on single frames,
so that we do not need a data association process along video frames. This is
because we associate the height priors with the image region sizes at image
places where map features projections fall within the object detection regions.
We present very promising results of this approach obtained on several
experiments with different object classes.Comment: Int. Workshop on Visual Odometry, CVPR, (July 2017
Restoration of Poissonian Images Using Alternating Direction Optimization
Much research has been devoted to the problem of restoring Poissonian images,
namely for medical and astronomical applications. However, the restoration of
these images using state-of-the-art regularizers (such as those based on
multiscale representations or total variation) is still an active research
area, since the associated optimization problems are quite challenging. In this
paper, we propose an approach to deconvolving Poissonian images, which is based
on an alternating direction optimization method. The standard regularization
(or maximum a posteriori) restoration criterion, which combines the Poisson
log-likelihood with a (non-smooth) convex regularizer (log-prior), leads to
hard optimization problems: the log-likelihood is non-quadratic and
non-separable, the regularizer is non-smooth, and there is a non-negativity
constraint. Using standard convex analysis tools, we present sufficient
conditions for existence and uniqueness of solutions of these optimization
problems, for several types of regularizers: total-variation, frame-based
analysis, and frame-based synthesis. We attack these problems with an instance
of the alternating direction method of multipliers (ADMM), which belongs to the
family of augmented Lagrangian algorithms. We study sufficient conditions for
convergence and show that these are satisfied, either under total-variation or
frame-based (analysis and synthesis) regularization. The resulting algorithms
are shown to outperform alternative state-of-the-art methods, both in terms of
speed and restoration accuracy.Comment: 12 pages, 12 figures, 2 tables. Submitted to the IEEE Transactions on
Image Processin
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