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 issue of image haze removal has attracted wide attention in recent years.However, most existing haze removal methods cannot restore the scene with clearblue sky, since the color and texture information of the object in the originalhaze image is insufficient. To remedy this, we propose a cycle generativeadversarial network to construct a novel end-to-end image dehaze model. Weadopt outdoor image datasets to train our model, which includes a set ofreal-world unpaired image dataset and a set of paired image dataset to ensurethat the generated images are close to the real scene. Based on the cyclestructure, our model adds four different kinds of loss function to constrainthe effect including adversarial loss, cycle consistency loss, photorealismloss and paired L1 loss. These four constraints can improve the overall qualityof such degraded images for better visual appeal and ensure reconstruction ofimages to keep from distortion. The proposed model could remove the haze ofimages and also restore the sky of images to be clean and blue (like capturedin a sunny weather).

See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images