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

Traditional radar imaging methods suffer from the problems of low resolutionand poor noise suppression. We propose a new radar imaging method based onSelf-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). Theoriginal radar image as the input of net. The net is trained to learn themapping function between the original radar image and the high quality radarimage. However, the high quality radar image cant be obtained. We solve thisproblem by used the sparsity of radar image. The original radar image and imagewith the zeros value as the reference of net. Ours net dont need a lot of datato train. Real radar data are used to evaluate the performance of the proposedmethod. The experimental results demonstrate the superiority of the proposedmethod

Self-supervised Enhanced Radar Imaging Based on Deep-Learning-Assisted Compressed Sensing