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

Compared to other severe weather image restoration tasks, single imagedesnowing is a more challenging task. This is mainly due to the diversity andirregularity of snow shape, which makes it extremely difficult to restoreimages in snowy scenes. Moreover, snow particles also have a veiling effectsimilar to haze or mist. Although current works can effectively remove snowparticles with various shapes, they also bring distortion to the restoredimage. To address these issues, we propose a novel single image desnowingnetwork called Star-Net. First, we design a Star type Skip Connection (SSC) toestablish information channels for all different scale features, which can dealwith the complex shape of snow particles.Second, we present a Multi-StageInteractive Transformer (MIT) as the base module of Star-Net, which is designedto better understand snow particle shapes and to address image distortion byexplicitly modeling a variety of important image recovery features. Finally, wepropose a Degenerate Filter Module (DFM) to filter the snow particle and snowfog residual in the SSC on the spatial and channel domains. Extensiveexperiments show that our Star-Net achieves state-of-the-art snow removalperformances on three standard snow removal datasets and retains the originalsharpness of the images.

Star-Net: Improving Single Image Desnowing Model With More Efficient Connection and Diverse Feature Interaction