Laplacian Colormaps: a Framework for Structure-preserving Color Transformations

Simple item page
dc.contributor.authorEynard, Davideen_US
dc.contributor.authorKovnatsky, Artiomen_US
dc.contributor.authorBronstein, Michael M.en_US
dc.contributor.editorB. Levy and J. Kautzen_US
dc.date.accessioned2015-03-03T12:27:45Z
dc.date.available2015-03-03T12:27:45Z
dc.date.issued2014en_US
dc.description.abstractMappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people. Simple color transformations often result in information loss and ambiguities, and one wishes to find an image-specific transformation that would preserve as much as possible the structure of the original image in the target color space. In this paper, we propose Laplacian colormaps, a generic framework for structure-preserving color transformations between images. We use the image Laplacian to capture the structural information, and show that if the color transformation between two images preserves the structure, the respective Laplacians have similar eigenvectors, or in other words, are approximately jointly diagonalizable. Employing the relation between joint diagonalizability and commutativity of matrices, we use Laplacians commutativity as a criterion of color mapping quality and minimize it w.r.t. the parameters of a color transformation to achieve optimal structure preservation. We show numerous applications of our approach, including color-to-gray conversion, gamut mapping, multispectral image fusion, and image optimization for color deficient viewers.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12295en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleLaplacian Colormaps: a Framework for Structure-preserving Color Transformationsen_US
Files
Collections
33-Issue 2
Full Papers 2014 - CGF 33-Issue 2