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

Recent multi-view multimedia applications struggle between high-resolution(HR) visual experience and storage or bandwidth constraints. Therefore, thispaper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims toincrease the resolution of multi-view images captured from the same scene. Onesolution is to apply image or video super-resolution (SR) methods toreconstruct HR results from the low-resolution (LR) input view. However, thesemethods cannot handle large-angle transformations between views and leverageinformation in all multi-view images. To address these problems, we propose theMVSRnet, which uses geometry information to extract sharp details from all LRmulti-view to support the SR of the LR input view. Specifically, the proposedGeometry-Aware Reference Synthesis module in MVSRnet uses geometry informationand all multi-view LR images to synthesize pixel-aligned HR reference images.Then, the proposed Dynamic High-Frequency Search network fully exploits thehigh-frequency textural details in reference images for SR. Extensiveexperiments on several benchmarks show that our method significantly improvesover the state-of-the-art approaches.

Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution