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

Chongyi LI, Huazhu FU*, Runmin CONG*, Zechao LI, Qianqian XU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

11 Citations (Scopus)

Abstract

Retinal images have been widely used by clinicians for early diagnosis of ocular diseases. However, the quality of retinal images is often clinically unsatisfactory due to eye lesions and imperfect imaging process. One of the most challenging quality degradation issues in retinal images is non-uniform which hinders the pathological information and further impairs the diagnosis of ophthalmologists and computer-aided analysis. To address this issue, we propose a 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 hierarchical feature representations, a non-local context module that models the context information, and a feature decoder module that recovers the details and spatial dimension. Additionally, the symmetric skip-connections between the encoder module and the decoder module provide long-range information compensation and reuse. Extensive experiments demonstrate that the proposed method can effectively remove the non-uniform illumination on retinal images while well preserving the image details and color. We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.

Original languageEnglish
Title of host publicationProceedings : 28th ACM International Conference on Multimedia, MM 2020
PublisherAssociation for Computing Machinery, Inc
Pages1478-1487
Number of pages10
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes
Event28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
Duration: 12 Oct 202016 Oct 2020

Conference

Conference28th ACM International Conference on Multimedia, MM 2020
Country/TerritoryUnited States
CityVirtual, Online
Period12/10/2016/10/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

  • deep learning
  • non-uniform illumination removal
  • retinal image enhancement

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