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

Fundus photography is a routine examination in clinics to diagnose andmonitor ocular diseases. However, for cataract patients, the fundus imagealways suffers quality degradation caused by the clouding lens. The degradationprevents reliable diagnosis by ophthalmologists or computer-aided systems. Toimprove the certainty in clinical diagnosis, restoration algorithms have beenproposed to enhance the quality of fundus images. Unfortunately, challengesremain in the deployment of these algorithms, such as collecting sufficienttraining data and preserving retinal structures. In this paper, to circumventthe strict deployment requirement, a structure-consistent restoration network(SCR-Net) for cataract fundus images is developed from synthesized data thatshares an identical structure. A cataract simulation model is firstly designedto collect synthesized cataract sets (SCS) formed by cataract fundus imagessharing identical structures. Then high-frequency components (HFCs) areextracted from the SCS to constrain structure consistency such that thestructure preservation in SCR-Net is enforced. The experiments demonstrate theeffectiveness of SCR-Net in the comparison with state-of-the-art methods andthe follow-up clinical applications. The code is available athttps://github.com/liamheng/ArcNet-Medical-Image-Enhancement.

Structure-consistent Restoration Network for Cataract Fundus Image Enhancement