13,820 research outputs found
Image Inpainting by Hyperbolic Selection of Pixels for Two Dimensional Bicubic Interpolations
Image inpainting is a restoration process which has numerous applications.
Restoring of scanned old images with scratches, or removing objects in images
are some of inpainting applications. Different approaches have been used for
implementation of inpainting algorithms. Interpolation approaches only consider
one direction for this purpose. In this paper we present a new perspective to
image inpainting. We consider multiple directions and apply both
one-dimensional and two-dimensional bicubic interpolations. Neighboring pixels
are selected in a hyperbolic formation to better preserve corner pixels. We
compare our work with recent inpainting approaches to show our superior
results
Image inpainting based on coherence transport with adapted distance functions
We discuss an extension of our method Image Inpainting Based on Coherence Transport. For the latter method the pixels of the inpainting domain have to be serialized into an ordered list. Up till now, to induce the serialization we have used the distance to boundary map. But there are inpainting problems where the distance to boundary serialization causes unsatisfactory inpainting results. In the present work we demonstrate cases where we can resolve the difficulties by employing other distance functions which better suit the problem at hand
Dealing with missing data: An inpainting application to the MICROSCOPE space mission
Missing data are a common problem in experimental and observational physics.
They can be caused by various sources, either an instrument's saturation, or a
contamination from an external event, or a data loss. In particular, they can
have a disastrous effect when one is seeking to characterize a
colored-noise-dominated signal in Fourier space, since they create a spectral
leakage that can artificially increase the noise. It is therefore important to
either take them into account or to correct for them prior to e.g. a
Least-Square fit of the signal to be characterized. In this paper, we present
an application of the {\it inpainting} algorithm to mock MICROSCOPE data; {\it
inpainting} is based on a sparsity assumption, and has already been used in
various astrophysical contexts; MICROSCOPE is a French Space Agency mission,
whose launch is expected in 2016, that aims to test the Weak Equivalence
Principle down to the level. We then explore the {\it inpainting}
dependence on the number of gaps and the total fraction of missing values. We
show that, in a worst-case scenario, after reconstructing missing values with
{\it inpainting}, a Least-Square fit may allow us to significantly measure a
Equivalence Principle violation signal, which is
sufficiently close to the MICROSCOPE requirements to implement {\it inpainting}
in the official MICROSCOPE data processing and analysis pipeline. Together with
the previously published KARMA method, {\it inpainting} will then allow us to
independently characterize and cross-check an Equivalence Principle violation
signal detection down to the level.Comment: Accepted for publication in Physical Review D. 12 pages, 6 figure
Adversarial Inpainting of Medical Image Modalities
Numerous factors could lead to partial deteriorations of medical images. For
example, metallic implants will lead to localized perturbations in MRI scans.
This will affect further post-processing tasks such as attenuation correction
in PET/MRI or radiation therapy planning. In this work, we propose the
inpainting of medical images via Generative Adversarial Networks (GANs). The
proposed framework incorporates two patch-based discriminator networks with
additional style and perceptual losses for the inpainting of missing
information in realistically detailed and contextually consistent manner. The
proposed framework outperformed other natural image inpainting techniques both
qualitatively and quantitatively on two different medical modalities.Comment: To be submitted to ICASSP 201
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