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

The spatial and spectral degradation functions are critical to hyper- andmulti-spectral image fusion. However, few work has been payed on the estimationof the degradation functions. To learn the spatial response function and thepoint spread function from the image pairs to be fused, we propose a Dirichletnetwork, where both functions are properly constrained. Specifically, thespatial response function is constrained with positivity, while the Dirichletdistribution along with a total variation is imposed on the point spreadfunction. To the best of our knowledge, the neural netwrok and the Dirichletregularization are exclusively investigated, for the first time, to estimatethe degradation functions. Both image degradation and fusion experimentsdemonstrate the effectiveness and superiority of the proposed Dirichletnetwork.

DiriNet: A network to estimate the spatial and spectral degradation functions