1,113 research outputs found
Compressive Earth Observatory: An Insight from AIRS/AMSU Retrievals
We demonstrate that the global fields of temperature, humidity and
geopotential heights admit a nearly sparse representation in the wavelet
domain, offering a viable path forward to explore new paradigms of
sparsity-promoting data assimilation and compressive recovery of land
surface-atmospheric states from space. We illustrate this idea using retrieval
products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave
Sounding Unit (AMSU) on board the Aqua satellite. The results reveal that the
sparsity of the fields of temperature is relatively pressure-independent while
atmospheric humidity and geopotential heights are typically sparser at lower
and higher pressure levels, respectively. We provide evidence that these
land-atmospheric states can be accurately estimated using a small set of
measurements by taking advantage of their sparsity prior.Comment: 12 pages, 8 figures, 1 tabl
A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal
Unveiling meaningful geophysical information from seismic data requires to
deal with both random and structured "noises". As their amplitude may be
greater than signals of interest (primaries), additional prior information is
especially important in performing efficient signal separation. We address here
the problem of multiple reflections, caused by wave-field bouncing between
layers. Since only approximate models of these phenomena are available, we
propose a flexible framework for time-varying adaptive filtering of seismic
signals, using sparse representations, based on inaccurate templates. We recast
the joint estimation of adaptive filters and primaries in a new convex
variational formulation. This approach allows us to incorporate plausible
knowledge about noise statistics, data sparsity and slow filter variation in
parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a
constrained minimization problem that alleviates standard regularization issues
in finding hyperparameters. The approach demonstrates significantly good
performance in low signal-to-noise ratio conditions, both for simulated and
real field seismic data
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to
that of conventional optical cameras. Recent work has explored the idea of
improving the resolution of depth using higher resolution intensity as a side
information. In this paper, we demonstrate that further incorporating temporal
information in videos can significantly improve the results. In particular, we
propose a novel approach that improves depth resolution, exploiting the
space-time redundancy in the depth and intensity using motion-adaptive low-rank
regularization. Experiments confirm that the proposed approach substantially
improves the quality of the estimated high-resolution depth. Our approach can
be a first component in systems using vision techniques that rely on high
resolution depth information
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