Color-aware regularization for gradient domain image manipulation

Fanbo Deng, Seon Joo Kim, Yu Wing Tai, Michael S. Brown

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

2 Citations (Scopus)


We propose a color-aware regularization for use with gradient domain image manipulation to avoid color shift artifacts. Our work is motivated by the observation that colors of objects in natural images typically follow distinct distributions in the color space. Conventional regularization methods ignore these distributions which can lead to undesirable colors appearing in the final output. Our approach uses an anisotropic Mahalanobis distance to control output colors to better fit original distributions. Our color-aware regularization is simple, easy to implement, and does not introduce significant computational overhead. To demonstrate the effectiveness of our method, we show the results with and without our color-aware regularization on three gradient domain tasks: gradient transfer, gradient boosting, and saliency sharpening.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages14
EditionPART 4
Publication statusPublished - 2013
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 2012 Nov 52012 Nov 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7727 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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