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

Benefiting from a relatively larger aperture's angle, and in combination witha wide transmitting bandwidth, near-field synthetic aperture radar (SAR)provides a high-resolution image of a target's scattering distribution-hotspots. Meanwhile, imaging result suffers inevitable degradation from sidelobes,clutters, and noises, hindering the information retrieval of the target. Torestore the image, current methods make simplified assumptions; for example,the point spread function (PSF) is spatially consistent, the target consists ofsparse point scatters, etc. Thus, they achieve limited restoration performancein terms of the target's shape, especially for complex targets. To addressthese issues, a preliminary study is conducted on restoration with the recentpromising deep learning inverse technique in this work. We reformulate thedegradation model into a spatially variable complex-convolution model, wherethe near-field SAR's system response is considered. Adhering to it, amodel-based deep learning network is designed to restore the image. A simulateddegraded image dataset from multiple complex target models is constructed tovalidate the network. All the images are formulated using the electromagneticsimulation tool. Experiments on the dataset reveal their effectiveness.Compared with current methods, superior performance is achieved regarding thetarget's shape and energy estimation.

Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study