2,127,962 research outputs found
CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization
This paper proposes a spatial-Radon domain CT image reconstruction model
based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model
combines the idea of joint image and Radon domain inpainting model of
\cite{Dong2013X} and that of the data-driven tight frames for image denoising
\cite{cai2014data}. It is different from existing models in that both CT image
and its corresponding high quality projection image are reconstructed
simultaneously using sparsity priors by tight frames that are adaptively
learned from the data to provide optimal sparse approximations. An alternative
minimization algorithm is designed to solve the proposed model which is
nonsmooth and nonconvex. Convergence analysis of the algorithm is provided.
Numerical experiments showed that the SRD-DDTF model is superior to the model
by \cite{Dong2013X} especially in recovering some subtle structures in the
images
Searching for comets on the World Wide Web: The orbit of 17P/Holmes from the behavior of photographers
We performed an image search for "Comet Holmes," using the Yahoo Web search
engine, on 2010 April 1. Thousands of images were returned. We astrometrically
calibrated---and therefore vetted---the images using the Astrometry.net system.
The calibrated image pointings form a set of data points to which we can fit a
test-particle orbit in the Solar System, marginalizing over image dates and
detecting outliers. The approach is Bayesian and the model is, in essence, a
model of how comet astrophotographers point their instruments. In this work, we
do not measure the position of the comet within each image, but rather use the
celestial position of the whole image to infer the orbit. We find very strong
probabilistic constraints on the orbit, although slightly off the JPL
ephemeris, probably due to limitations of our model. Hyperparameters of the
model constrain the reliability of date meta-data and where in the image
astrophotographers place the comet; we find that ~70 percent of the meta-data
are correct and that the comet typically appears in the central third of the
image footprint. This project demonstrates that discoveries and measurements
can be made using data of extreme heterogeneity and unknown provenance. As the
size and diversity of astronomical data sets continues to grow, approaches like
ours will become more essential. This project also demonstrates that the Web is
an enormous repository of astronomical information; and that if an object has
been given a name and photographed thousands of times by observers who post
their images on the Web, we can (re-)discover it and infer its dynamical
properties.Comment: As published. Changes in v2: data-driven initialization rather than
JPL; added figures; clarified tex
Self-Supervised Intrinsic Image Decomposition
Intrinsic decomposition from a single image is a highly challenging task, due
to its inherent ambiguity and the scarcity of training data. In contrast to
traditional fully supervised learning approaches, in this paper we propose
learning intrinsic image decomposition by explaining the input image. Our
model, the Rendered Intrinsics Network (RIN), joins together an image
decomposition pipeline, which predicts reflectance, shape, and lighting
conditions given a single image, with a recombination function, a learned
shading model used to recompose the original input based off of intrinsic image
predictions. Our network can then use unsupervised reconstruction error as an
additional signal to improve its intermediate representations. This allows
large-scale unlabeled data to be useful during training, and also enables
transferring learned knowledge to images of unseen object categories, lighting
conditions, and shapes. Extensive experiments demonstrate that our method
performs well on both intrinsic image decomposition and knowledge transfer.Comment: NIPS 2017 camera-ready version, project page:
http://rin.csail.mit.edu
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