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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Solar activity is one of the main drivers of variability in our solar systemand the key source of space weather phenomena that affect Earth and near Earthspace. The extensive record of high resolution extreme ultraviolet (EUV)observations from the Solar Dynamics Observatory (SDO) offers an unprecedented,very large dataset of solar images. In this work, we make use of thiscomprehensive dataset to investigate capabilities of current state-of-the-artgenerative models to accurately capture the data distribution behind theobserved solar activity states. Starting from StyleGAN-based methods, weuncover severe deficits of this model family in handling fine-scale details ofsolar images when training on high resolution samples, contrary to training onnatural face images. When switching to the diffusion based generative modelfamily, we observe strong improvements of fine-scale detail generation. For theGAN family, we are able to achieve similar improvements in fine-scalegeneration when turning to ProjectedGANs, which uses multi-scale discriminatorswith a pre-trained frozen feature extractor. We conduct ablation studies toclarify mechanisms responsible for proper fine-scale handling. Usingdistributed training on supercomputers, we are able to train generative modelsfor up to 1024x1024 resolution that produce high quality samplesindistinguishable to human experts, as suggested by the evaluation we conduct.We make all code, models and workflows used in this study publicly available at\url{https://github.com/SLAMPAI/generative-models-for-highres-solar-images}.

A Comparative Study on Generative Models for High Resolution Solar Observation Imaging