Title
Intrinsic Decompositions for Image Editing.
Abstract
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.
Year
DOI
Venue
2017
10.1111/cgf.13149
Comput. Graph. Forum
Field
DocType
Volume
Computer vision,Surface geometry,Computer science,Image editing,Ranging,Artificial intelligence,Image-based modeling and rendering,Reflectivity,Prior probability,Computer graphics,Instrumental and intrinsic value
Journal
36
Issue
ISSN
Citations 
2
0167-7055
11
PageRank 
References 
Authors
0.51
18
4
Name
Order
Citations
PageRank
Nicolas Bonneel137722.50
Balazs Kovacs2693.87
Sylvain Paris32494113.53
Kavita Bala42046138.75