Intrinsic Decompositions for Image Editing

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 illumin...

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Bibliographic Details
Published in:Computer graphics forum Vol. 36; no. 2; pp. 593 - 609
Main Authors: Bonneel, Nicolas, Kovacs, Balazs, Paris, Sylvain, Bala, Kavita
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.05.2017
Wiley
Series:Eurographics State of the Art Reports 2017
Subjects:
Categories and Subject Descriptors (according to ACM CCS)
Computer graphics
Computer Science
Computer vision
Computer Vision and Pattern Recognition
Decomposition
Editing
I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation
Illumination
Image Processing
Image reconstruction
Performance evaluation
Reflectance
Representations
Scene analysis
Signal and Image Processing
Software reviews
Surface geometry
Image editing
Intrinsic Images
ISSN:0167-7055, 1467-8659
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Summary: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.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13149