45,319 research outputs found
From coarse wall measurements to turbulent velocity fields through deep learning
This work evaluates the applicability of super-resolution generative
adversarial networks (SRGANs) as a methodology for the reconstruction of
turbulent-flow quantities from coarse wall measurements. The method is applied
both for the resolution enhancement of wall fields and the estimation of
wall-parallel velocity fields from coarse wall measurements of shear stress and
pressure. The analysis has been carried out with a database of a turbulent
open-channel flow with friction Reynolds number generated
through direct numerical simulation. Coarse wall measurements have been
generated with three different downsampling factors from the
high-resolution fields, and wall-parallel velocity fields have been
reconstructed at four inner-scaled wall-normal distances .
We first show that SRGAN can be used to enhance the resolution of coarse wall
measurements. If compared with direct reconstruction from the sole coarse wall
measurements, SRGAN provides better instantaneous reconstructions, both in
terms of mean-squared error and spectral-fractional error. Even though lower
resolutions in the input wall data make it more challenging to achieve highly
accurate predictions, the proposed SRGAN-based network yields very good
reconstruction results. Furthermore, it is shown that even for the most
challenging cases the SRGAN is capable of capturing the large-scale structures
that populate the flow. The proposed novel methodology has great potential for
closed-loop control applications relying on non-intrusive sensing
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
Super-resolution is a classical problem in image processing, with numerous
applications to remote sensing image enhancement. Here, we address the
super-resolution of irregularly-sampled remote sensing images. Using an optimal
interpolation as the low-resolution reconstruction, we explore locally-adapted
multimodal convolutional models and investigate different dictionary-based
decompositions, namely based on principal component analysis (PCA), sparse
priors and non-negativity constraints. We consider an application to the
reconstruction of sea surface height (SSH) fields from two information sources,
along-track altimeter data and sea surface temperature (SST) data. The reported
experiments demonstrate the relevance of the proposed model, especially
locally-adapted parametrizations with non-negativity constraints, to outperform
optimally-interpolated reconstructions.Comment: 4 pages, 3 figure
Statistical performance analysis of a fast super-resolution technique using noisy translations
It is well known that the registration process is a key step for
super-resolution reconstruction. In this work, we propose to use a
piezoelectric system that is easily adaptable on all microscopes and telescopes
for controlling accurately their motion (down to nanometers) and therefore
acquiring multiple images of the same scene at different controlled positions.
Then a fast super-resolution algorithm \cite{eh01} can be used for efficient
super-resolution reconstruction. In this case, the optimal use of images
for a resolution enhancement factor is generally not enough to obtain
satisfying results due to the random inaccuracy of the positioning system. Thus
we propose to take several images around each reference position. We study the
error produced by the super-resolution algorithm due to spatial uncertainty as
a function of the number of images per position. We obtain a lower bound on the
number of images that is necessary to ensure a given error upper bound with
probability higher than some desired confidence level.Comment: 15 pages, submitte
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is based on the assumption that the co-supports of
corresponding bimodal image structures are aligned when computed by a suitable
pair of analysis operators. No analytic form of such operators exist and we
propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator
that is universally applicable to natural scenes. We use this to exploit the
bimodal co-sparse analysis model as a prior for solving inverse problems, which
leads to an efficient algorithm for depth map super-resolution.Comment: 13 pages, 4 figure
Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.
Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity
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