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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a novel Learned Alternating Minimization Algorithm (LAMA) fordual-domain sparse-view CT image reconstruction. LAMA is naturally induced by avariational model for CT reconstruction with learnable nonsmooth nonconvexregularizers, which are parameterized as composite functions of deep networksin both image and sinogram domains. To minimize the objective of the model, weincorporate the smoothing technique and residual learning architecture into thedesign of LAMA. We show that LAMA substantially reduces network complexity,improves memory efficiency and reconstruction accuracy, and is provablyconvergent for reliable reconstructions. Extensive numerical experimentsdemonstrate that LAMA outperforms existing methods by a wide margin on multiplebenchmark CT datasets.

Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction