English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Deep convolutional neural networks have been a popular tool for imagegeneration and restoration. The performance of these networks is related to thecapability of learning realistic features from a large dataset. In this work,we applied the problem of inpainting non-Gaussian signal, in the context ofGalactic diffuse emissions at the millimetric and sub-millimetric regimes,specifically Synchrotron and Thermal Dust emission. Both of them are affectedby contamination at small angular scales due to extra-galactic radio sources(the former) and to dusty star-forming galaxies (the latter). We consider theperformances of a nearest-neighbors inpainting technique and compare it withtwo novels methodologies relying on generative Neural Networks. We show thatthe generative network is able to reproduce the statistical properties of theground truth signal more consistently with high confidence level. The PythonInpainter for Cosmological and AStrophysical SOurces (PICASSO) is a packageencoding a suite of inpainting methods described in this work and has been madepublicly available.

Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network