Fast shadow removal using adaptive multi-scale illumination transfer

Chunxia Xiao, Ruiyun She, Donglin Xiao, Kwan-Liu Ma

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Secondly, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi-scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow-free results. In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Second, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi-scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow free results.

Original languageEnglish (US)
Pages (from-to)207-218
Number of pages12
JournalComputer Graphics Forum
Volume32
Issue number8
DOIs
StatePublished - Jan 1 2013

Keywords

  • gaussian mixture model
  • I.3.3 [Computer Graphics]: Picture/Image Generation - Line and curve generation
  • illumination transfer
  • multi-scale
  • shadow removal

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design

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