396,016 research outputs found
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
We show that denoising of 3D point clouds can be learned unsupervised,
directly from noisy 3D point cloud data only. This is achieved by extending
recent ideas from learning of unsupervised image denoisers to unstructured 3D
point clouds. Unsupervised image denoisers operate under the assumption that a
noisy pixel observation is a random realization of a distribution around a
clean pixel value, which allows appropriate learning on this distribution to
eventually converge to the correct value. Regrettably, this assumption is not
valid for unstructured points: 3D point clouds are subject to total noise, i.
e., deviations in all coordinates, with no reliable pixel grid. Thus, an
observation can be the realization of an entire manifold of clean 3D points,
which makes a na\"ive extension of unsupervised image denoisers to 3D point
clouds impractical. Overcoming this, we introduce a spatial prior term, that
steers converges to the unique closest out of the many possible modes on a
manifold. Our results demonstrate unsupervised denoising performance similar to
that of supervised learning with clean data when given enough training examples
- whereby we do not need any pairs of noisy and clean training data.Comment: Proceedings of ICCV 201
Unbiased image reconstruction as an inverse problem
An unbiased method for improving the resolution of astronomical images is
presented. The strategy at the core of this method is to establish a linear
transformation between the recorded image and an improved image at some
desirable resolution. In order to establish this transformation only the actual
point spread function and a desired point spread function need be known. Any
image actually recorded is not used in establishing the linear transformation
between the recorded and improved image. This method has a number of advantages
over other methods currently in use. It is not iterative which means it is not
necessary to impose any criteria, objective or otherwise, to stop the
iterations. The method does not require an artificial separation of the image
into ``smooth'' and ``point-like'' components, and thus is unbiased with
respect to the character of structures present in the image. The method
produces a linear transformation between the recorded image and the deconvolved
image and therefore the propagation of pixel-by-pixel flux error estimates into
the deconvolved image is trivial. It is explicitly constrained to preserve
photometry.Comment: 11 pages, TeX, uses mn.tex epsf.tex, accepted for publication in
MNRA
Fundamental Limitations of Pixel Based Image Deconvolution in Radio Astronomy
Deconvolution is essential for radio interferometric imaging to produce
scientific quality data because of finite sampling in the Fourier plane. Most
deconvolution algorithms are based on CLEAN which uses a grid of image pixels,
or clean components. A critical matter in this process is the selection of
pixel size for optimal results in deconvolution. As a rule of thumb, the pixel
size is chosen smaller than the resolution dictated by the interferometer. For
images consisting of unresolved (or point like) sources, this approach yields
optimal results. However, for sources that are not point like, in particular
for partially resolved sources, the selection of right pixel size is still an
open issue. In this paper, we investigate the limitations of pixelization in
deconvolving extended sources. In particular, we pursue the usage of
orthonormal basis functions to model extended sources yielding better results
than by using clean components.Comment: 4 pages, 5 figures, the 6th IEEE Sensor Array and Multichannel Signal
Processing worksho
Surface projection for mixed pixel restoration
Amplitude modulated full-field range-imagers are measurement devices that determine the range to an object simultaneously for each pixel in the scene, but due to the nature of this operation, they commonly suffer from the significant problem of mixed pixels. Once mixed pixels are identified a common procedure is to remove them from the scene; this solution is not ideal as the captured point cloud may become damaged. This paper introduces an alternative approach, in which mixed pixels are projected onto the surface that they should belong. This is achieved by breaking the area around an identified mixed pixel into two classes. A parametric surface is then fitted to the class closest to the mixed pixel, with this mixed pixel then being project onto this surface. The restoration procedure was tested using twelve simulated scenes designed to determine its accuracy and robustness. For these simulated scenes, 93% of the mixed pixels were restored to the surface to which they belong. This mixed pixel restoration process is shown to be accurate and robust for both simulated and real world scenes, thus provides a reliable alternative to removing mixed pixels that can be easily adapted to any mixed pixel detection algorithm
The Photometry of Undersampled Point Spread Functions
An undersampled point spread function may interact with the microstructure of
a solid-state detector such that the total flux detected can depend sensitively
on where the PSF center falls within a pixel. Such intra-pixel sensitivity
variations will not be corrected by flat field calibration and may limit the
accuracy of stellar photometry conducted with undersampled images, as are
typical for Hubble Space Telescope observations. The total flux in a stellar
image can vary by up to 0.03 mag in F555W WFC images depending on how it is
sampled, for example. For NIC3, these variations are especially strong, up to
0.39 mag, strongly limiting its use for stellar photometry. Intra-pixel
sensitivity variations can be corrected for, however, by constructing a
well-sampled PSF from a dithered data set. The reconstructed PSF is the
convolution of the optical PSF with the pixel response. It can be evaluated at
any desired fractional pixel location to generate a table of photometric
corrections as a function of relative PSF centroid. A caveat is that the
centroid of an undersampled PSF can also be affected by the pixel response
function, thus sophisticated centroiding methods, such as cross-correlating the
observed PSF with its fully-sampled counterpart, are required to derive the
proper photometric correction.Comment: 20 pages, 14 postscript figures, submitted to the PAS
Missing Stellar Mass in SED Fitting: Spatially Unresolved Photometry can Underestimate Galaxy Masses
We fit model spectral energy distributions to each pixel in 67 nearby
(=0.0057) galaxies using broadband photometry from the Sloan Digital Sky
Survey and GALEX. For each galaxy, we compare the stellar mass derived by
summing the mass of each pixel to that found from fitting the entire galaxy
treated as an unresolved point source. We find that, while the pixel-by-pixel
and unresolved masses of galaxies with low specific star formation rates (such
as ellipticals and lenticulars) are in rough agreement, the unresolved mass
estimate for star-forming galaxies is systematically lower then the measurement
from spatially-resolved photometry. The discrepancy is strongly correlated with
sSFR, with the highest sSFRs in our sample having masses underestimated by 25%
(0.12 dex) when treated as point sources. We found a simple relation to
statistically correct mass estimates derived from unresolved broad-band SED
fitting to the resolved mass estimates: m_{resolved} =
m_{unresolved}/(-0.057log(sSFR) + 0.34) where sSFR is in units of yr^{-1}. We
study the effect of varying spatial resolution by degrading the image
resolution of the largest images and find a sharp decrease in the
pixel-by-pixel mass estimate at a physical scale of approximately 3 kpc, which
is comparable to spiral arm widths. The effects we observe are consistent with
the "outshining" idea which posits that the youngest stellar populations mask
more massive, older -- and thus fainter -- stellar populations. Although the
presence of strong dust lanes can also lead to a drastic difference between
resolved and unresolved mass estimates (up to 45% or 0.3 dex) for any
individual galaxy, we found that resolving dust does not affect mass estimates
on average. The strong correlation between mass discrepancy and sSFR is thus
most likely due to the outshining systematic bias.Comment: 13 pages, 8 figures, accepted for publication in MNRA
Pixel Detectors
Pixel detectors for precise particle tracking in high energy physics have
been developed to a level of maturity during the past decade. Three of the LHC
detectors will use vertex detectors close to the interaction point based on the
hybrid pixel technology which can be considered the state of the art in this
field of instrumentation. A development period of almost 10 years has resulted
in pixel detector modules which can stand the extreme rate and timing
requirements as well as the very harsh radiation environment at the LHC without
severe compromises in performance. From these developments a number of
different applications have spun off, most notably for biomedical imaging.
Beyond hybrid pixels, a number of monolithic or semi-monolithic developments,
which do not require complicated hybridization but come as single sensor/IC
entities, have appeared and are currently developed to greater maturity. Most
advanced in terms of maturity are so called CMOS active pixels and DEPFET
pixels. The present state in the construction of the hybrid pixel detectors for
the LHC experiments together with some hybrid pixel detector spin-off is
reviewed. In addition, new developments in monolithic or semi-monolithic pixel
devices are summarized.Comment: 14 pages, 38 drawings/photographs in 21 figure
Cosmic Ray Rejection by Linear Filtering of Single Images
We present a convolution-based algorithm for finding cosmic rays in single
well-sampled astronomical images. The spatial filter used is the point spread
function (approximated by a Gaussian) minus a scaled delta function, and cosmic
rays are identified by thresholding the filtered image. This filter searches
for features with significant power at spatial frequencies too high for
legitimate objects. Noise properties of the filtered image are readily
calculated, which allows us to compute the probability of rejecting a pixel not
contaminated by a cosmic ray (the false alarm probability). We demonstrate that
the false alarm probability for a pixel containing object flux will never
exceed the corresponding probability for a blank sky pixel, provided we choose
the convolution kernel appropriately. This allows confident rejection of cosmic
rays superposed on real objects. Identification of multiple-pixel cosmic ray
hits can be enhanced by running the algorithm iteratively, replacing flagged
pixels with the background level at each iteration.Comment: Accepted for publication in PASP (May 2000 issue). An iraf script
implementing the algorithm is available from the author, or from
http://sol.stsci.edu/~rhoads/ . 16 pages including 3 figures. Uses AASTeX
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