54,856 research outputs found
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
Iterative image reconstruction algorithms for optoacoustic tomography (OAT),
also known as photoacoustic tomography, have the ability to improve image
quality over analytic algorithms due to their ability to incorporate accurate
models of the imaging physics, instrument response, and measurement noise.
However, to date, there have been few reported attempts to employ advanced
iterative image reconstruction algorithms for improving image quality in
three-dimensional (3D) OAT. In this work, we implement and investigate two
iterative image reconstruction methods for use with a 3D OAT small animal
imager: namely, a penalized least-squares (PLS) method employing a quadratic
smoothness penalty and a PLS method employing a total variation norm penalty.
The reconstruction algorithms employ accurate models of the ultrasonic
transducer impulse responses. Experimental data sets are employed to compare
the performances of the iterative reconstruction algorithms to that of a 3D
filtered backprojection (FBP) algorithm. By use of quantitative measures of
image quality, we demonstrate that the iterative reconstruction algorithms can
mitigate image artifacts and preserve spatial resolution more effectively than
FBP algorithms. These features suggest that the use of advanced image
reconstruction algorithms can improve the effectiveness of 3D OAT while
reducing the amount of data required for biomedical applications
A Framework for SAR-Optical Stereogrammetry over Urban Areas
Currently, numerous remote sensing satellites provide a huge volume of
diverse earth observation data. As these data show different features regarding
resolution, accuracy, coverage, and spectral imaging ability, fusion techniques
are required to integrate the different properties of each sensor and produce
useful information. For example, synthetic aperture radar (SAR) data can be
fused with optical imagery to produce 3D information using stereogrammetric
methods. The main focus of this study is to investigate the possibility of
applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical
image pairs. For this purpose, the applicability of semi-global matching is
investigated in this unconventional multi-sensor setting. To support the image
matching by reducing the search space and accelerating the identification of
correct, reliable matches, the possibility of establishing an epipolarity
constraint for VHR SAR-optical image pairs is investigated as well. In
addition, it is shown that the absolute geolocation accuracy of VHR optical
imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be
improved by a multi-sensor block adjustment formulation based on rational
polynomial coefficients. Finally, the feasibility of generating point clouds
with a median accuracy of about 2m is demonstrated and confirms the potential
of 3D reconstruction from SAR-optical image pairs over urban areas.Comment: This is the pre-acceptance version, to read the final version, please
go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirec
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