64,539 research outputs found
Low-Cost Compressive Sensing for Color Video and Depth
A simple and inexpensive (low-power and low-bandwidth) modification is made
to a conventional off-the-shelf color video camera, from which we recover
{multiple} color frames for each of the original measured frames, and each of
the recovered frames can be focused at a different depth. The recovery of
multiple frames for each measured frame is made possible via high-speed coding,
manifested via translation of a single coded aperture; the inexpensive
translation is constituted by mounting the binary code on a piezoelectric
device. To simultaneously recover depth information, a {liquid} lens is
modulated at high speed, via a variable voltage. Consequently, during the
aforementioned coding process, the liquid lens allows the camera to sweep the
focus through multiple depths. In addition to designing and implementing the
camera, fast recovery is achieved by an anytime algorithm exploiting the
group-sparsity of wavelet/DCT coefficients.Comment: 8 pages, CVPR 201
Possible Application of Wavefront Coding to the LSST
Wavefront Coding has been applied as a means to increase the effective depth
of focus of optical systems. In this note I discuss the potential for this
technique to increase the depth of focus of the LSST and the resulting
advantages for the construction and operation of the facility, as well as
possible drawbacks. It may be possible to apply Wavefront Coding without
changing the current LSST design, in which case Wavefront Coding might merit
further study as a risk mitigation strategy.Comment: 6 pages, 2 figure
Optimising Spatial and Tonal Data for PDE-based Inpainting
Some recent methods for lossy signal and image compression store only a few
selected pixels and fill in the missing structures by inpainting with a partial
differential equation (PDE). Suitable operators include the Laplacian, the
biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The
quality of such approaches depends substantially on the selection of the data
that is kept. Optimising this data in the domain and codomain gives rise to
challenging mathematical problems that shall be addressed in our work.
In the 1D case, we prove results that provide insights into the difficulty of
this problem, and we give evidence that a splitting into spatial and tonal
(i.e. function value) optimisation does hardly deteriorate the results. In the
2D setting, we present generic algorithms that achieve a high reconstruction
quality even if the specified data is very sparse. To optimise the spatial
data, we use a probabilistic sparsification, followed by a nonlocal pixel
exchange that avoids getting trapped in bad local optima. After this spatial
optimisation we perform a tonal optimisation that modifies the function values
in order to reduce the global reconstruction error. For homogeneous diffusion
inpainting, this comes down to a least squares problem for which we prove that
it has a unique solution. We demonstrate that it can be found efficiently with
a gradient descent approach that is accelerated with fast explicit diffusion
(FED) cycles. Our framework allows to specify the desired density of the
inpainting mask a priori. Moreover, is more generic than other data
optimisation approaches for the sparse inpainting problem, since it can also be
extended to nonlinear inpainting operators such as EED. This is exploited to
achieve reconstructions with state-of-the-art quality.
We also give an extensive literature survey on PDE-based image compression
methods
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