User-assisted image shadow removal

Han Gong, Darren Cosker

Research output: Contribution to journalArticlepeer-review

16 Citations (SciVal)
229 Downloads (Pure)


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 piecewise 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
Number of pages9
JournalImage and Vision Computing
Early online date18 Apr 2017
Publication statusPublished - 1 Jun 2017


  • Colour correction
  • Curve fitting
  • Image shadow removal
  • Smoothing
  • User-assisted computer vision

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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