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Intrinsic Decompositions for Image Editing
Author(s) -
Bonneel Nicolas,
Kovacs Balazs,
Paris Sylvain,
Bala Kavita
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13149
Subject(s) - computer science , computer vision , artificial intelligence , image editing , computer graphics , image (mathematics) , representation (politics) , context (archaeology) , ground truth , prior probability , computer graphics (images) , global illumination , computational photography , graphics , image processing , rendering (computer graphics) , paleontology , bayesian probability , politics , political science , law , biology
Intrinsic images are a mid‐level representation of an image that decompose the image into reflectance and illumination layers. The reflectance layer captures the color/texture of surfaces in the scene, while the illumination layer captures shading effects caused by interactions between scene illumination and surface geometry. Intrinsic images have a long history in computer vision and recently in computer graphics, and have been shown to be a useful representation for tasks ranging from scene understanding and reconstruction to image editing. In this report, we review and evaluate past work on this problem. Specifically, we discuss each work in terms of the priors they impose on the intrinsic image problem. We introduce a new synthetic ground‐truth dataset that we use to evaluate the validity of these priors and the performance of the methods. Finally, we evaluate the performance of the different methods in the context of image‐editing applications.