SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images

Atif Anwer*, Samia Ainouz, Mohamad Naufal Mohamad Saad, Syed Saad Azhar Ali, Fabrice Meriaudeau

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. Most modern techniques proposed are inadequate at dealing with real-world images taken under uncontrolled conditions with the presence of complex textures, multiple objects, and bright colours, resulting in reduced accuracy and false positives. To detect specular pixels in a wide variety of real-world images independent of the number, colour, or type of illuminating source, we propose an efficient Specular Segmentation (SpecSeg) network based on the U-net architecture that is expeditious to train on nominal-sized datasets. The proposed network can detect pixels strongly affected by specular highlights with a high degree of precision, as shown by comparison with the state-of-the-art methods. The technique proposed is trained on publicly available datasets and tested using a large selection of real-world images with highly encouraging results.

Original languageEnglish
Article number6552
Issue number17
StatePublished - Sep 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.


  • image segmentation
  • specular highlights

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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