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/

Zendy Plus

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

Zendy Tools

Zendy Open

Blind motion deblurring is one of the most basic and challenging problems inimage processing and computer vision. It aims to recover a sharp image from itsblurred version knowing nothing about the blur process. Many existing methodsuse Maximum A Posteriori (MAP) or Expectation Maximization (EM) frameworks todeal with this kind of problems, but they cannot handle well the figh frequencyfeatures of natural images. Most recently, deep neural networks have beenemerging as a powerful tool for image deblurring. In this paper, we prove thatencoder-decoder architecture gives better results for image deblurring tasks.In addition, we propose a novel end-to-end learning model which refinesgenerative adversarial network by many novel training strategies so as totackle the problem of deblurring. Experimental results show that our model cancapture high frequency features well, and the results on benchmark dataset showthat proposed model achieves the competitive performance.

Blind Motion Deblurring with Cycle Generative Adversarial Networks