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

Removing the rain streaks from single image is still a challenging task,since the shapes and directions of rain streaks in the synthetic datasets arevery different from real images. Although supervised deep deraining networkshave obtained impressive results on synthetic datasets, they still cannotobtain satisfactory results on real images due to weak generalization of rainremoval capacity, i.e., the pre-trained models usually cannot handle new shapesand directions that may lead to over-derained/under-derained results. In thispaper, we propose a new semi-supervised GAN-based deraining network termedSemi-DerainGAN, which can use both synthetic and real rainy images in a uniformnetwork using two supervised and unsupervised processes. Specifically, asemi-supervised rain streak learner termed SSRML sharing the same parameters ofboth processes is derived, which makes the real images contribute more rainstreak information. To deliver better deraining results, we design a paireddiscriminator for distinguishing the real pairs from fake pairs. Note that wealso contribute a new real-world rainy image dataset Real200 to alleviate thedifference between the synthetic and real image do-mains. Extensive results onpublic datasets show that our model can obtain competitive performance,especially on real images.

Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network