User-Assisted Image Shadow Removal

Han Gong, Darren Cosker

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
9 Downloads (Pure)

Abstract

This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piece-wise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods.
Original languageEnglish
Pages (from-to)19–27
JournalImage and Vision Computing
Volume62
Early online date18 Apr 2017
DOIs
Publication statusPublished - Jun 2017

Keywords

  • image shadow removal
  • user-assisted computer vision
  • colour correction
  • curve fitting
  • smoothing
  • User-Aided Single Image Shadow Removal

    Gong, H., Cosker, D., Li, C. & Brown, M., 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME). p. 1-6 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    10 Citations (Scopus)

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