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 presence of non-homogeneous haze can cause scene blurring, colordistortion, low contrast, and other degradations that obscure texture details.Existing homogeneous dehazing methods struggle to handle the non-uniformdistribution of haze in a robust manner. The crucial challenge ofnon-homogeneous dehazing is to effectively extract the non-uniform distributionfeatures and reconstruct the details of hazy areas with high quality. In thispaper, we propose a novel self-paced semi-curricular attention network, calledSCANet, for non-homogeneous image dehazing that focuses on enhancinghaze-occluded regions. Our approach consists of an attention generator networkand a scene reconstruction network. We use the luminance differences of imagesto restrict the attention map and introduce a self-paced semi-curricularlearning strategy to reduce learning ambiguity in the early stages of training.Extensive quantitative and qualitative experiments demonstrate that our SCANetoutperforms many state-of-the-art methods. The code is publicly available athttps://github.com/gy65896/SCANet.

SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing