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

Retinal images have been widely used by clinicians for early diagnosis ofocular diseases. However, the quality of retinal images is often clinicallyunsatisfactory due to eye lesions and imperfect imaging process. One of themost challenging quality degradation issues in retinal images is non-uniformwhich hinders the pathological information and further impairs the diagnosis ofophthalmologists and computer-aided analysis.To address this issue, we proposea non-uniform illumination removal network for retinal image, called NuI-Go,which consists of three Recursive Non-local Encoder-Decoder Residual Blocks(NEDRBs) for enhancing the degraded retinal images in a progressive manner.Each NEDRB contains a feature encoder module that captures the hierarchicalfeature representations, a non-local context module that models the contextinformation, and a feature decoder module that recovers the details and spatialdimension. Additionally, the symmetric skip-connections between the encodermodule and the decoder module provide long-range information compensation andreuse. Extensive experiments demonstrate that the proposed method caneffectively remove the non-uniform illumination on retinal images while wellpreserving the image details and color. We further demonstrate the advantagesof the proposed method for improving the accuracy of retinal vesselsegmentation.

NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal