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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Generative adversarial network (GAN) for image super-resolution (SR) hasattracted enormous interests in recent years. However, the GAN-based SR methodsonly use image discriminator to distinguish SR images and high-resolution (HR)images. Image discriminator fails to discriminate images accurately since imagefeatures cannot be fully expressed. In this paper, we design a new GAN-based SRframework GAN-IMC which includes generator, image discriminator, morphologicalcomponent discriminator and color discriminator. The combination of multiplefeature discriminators improves the accuracy of image discrimination.Adversarial training between the generator and multi-feature discriminatorsforces SR images to converge with HR images in terms of data and featuresdistribution. Moreover, in some cases, feature enhancement of salient regionsis also worth considering. GAN-IMC is further optimized by weighted contentloss (GAN-IMCW), which effectively restores and enhances salient regions in SRimages. The effectiveness and robustness of our method are confirmed byextensive experiments on public datasets. Compared with state-of-the-artmethods, the proposed method not only achieves competitive Perceptual Index(PI) and Natural Image Quality Evaluator (NIQE) values but also obtainspleasant visual perception in image edge, texture, color and salient regions.

Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators