High-Resolution Reconstruction of Sparse Data from Dense Low-Resolution Spatio-Temporal Data
IEEE Transaction on Image Processing, 2003, in press.
A novel approach for reconstruction of sparse high-resolution data from
lower-resolution dense spatio-temporal data is introduced. The basic
idea is to compute the dense feature velocities from lower-resolution
data and project them to the corresponding high-resolution
data for computing the missing data. In this context, the basic
flow equation is solved for intensity, as opposed to feature velocities
at high resolution. Although the proposed technique is
generic, we have applied our approach to sea surface temperature (SST)
data at 18 km (low-resolution dense data)
for computing the feature velocities and at 4 km
(high-resolution sparse data) for interpolating the missing data.
At low resolution, computation of the flow field is
regularized and uses the incompressibility constraints for
tracking fluid motion. At high
resolution, computation of the intensity is regularized for continuity
across multiple frames.
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