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 profound accumulation of precipitation during intense rainfall events canmarkedly degrade the quality of images, leading to the erosion of texturaldetails. Despite the improvements observed in existing learning-based methodsspecialized for heavy rain removal, it is discerned that a significantproportion of these methods tend to overlook the precise reconstruction of theintricate details. In this work, we introduce a simple dual-stage progressiveenhancement network, denoted as DPENet, aiming to achieve effective derainingwhile preserving the structural accuracy of rain-free images. This approachcomprises two key modules, a rain streaks removal network (R$^2$Net) focusingon accurate rain removal, and a details reconstruction network (DRNet) designedto recover the textural details of rain-free images. Firstly, we introduce adilated dense residual block (DDRB) within R$^2$Net, enabling the aggregationof high-level and low-level features. Secondly, an enhanced residual pixel-wiseattention block (ERPAB) is integrated into DRNet to facilitate theincorporation of contextual information. To further enhance the fidelity of ourapproach, we employ a comprehensive loss function that accentuates both themarginal and regional accuracy of rain-free images. Extensive experimentsconducted on publicly available benchmarks demonstrates the noteworthyefficiency and effectiveness of our proposed DPENet. The source code andpre-trained models are currently available at\url{https://github.com/chdwyb/DPENet}.

From heavy rain removal to detail restoration: A faster and better network