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

Unpaired image denoising has achieved promising development over the last fewyears. Regardless of the performance, methods tend to heavily rely onunderlying noise properties or any assumption which is not always practical.Alternatively, if we can ground the problem from a structural perspectiverather than noise statistics, we can achieve a more robust solution. with suchmotivation, we propose a self-supervised denoising scheme that is unpaired andrelies on spatial degradation followed by a regularized refinement. Our methodshows considerable improvement over previous methods and exhibited consistentperformance over different data domains.

Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising