Affiliations 

  • 1 Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (LITIS), Normandie Université, UNIROUEN, UNIHAVRE, INSA Rouen, 76000 Rouen, France
  • 2 Centre for Intelligent Signal& Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
  • 3 Aerospace Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
  • 4 ImViA, Université Bourgogne-Franche-Comté, 71200 Le Creusot, France
Sensors (Basel), 2022 Aug 30;22(17).
PMID: 36081012 DOI: 10.3390/s22176552

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.