A Variational Framework for Retinex
International Journal of Computer Vision
Recovering Shading from Color Images
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
A local search approximation algorithm for k-means clustering
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometry—SoCG2002
Recovering Intrinsic Images from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating Intrinsic Component Images using Non-Linear Regression
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ACM SIGGRAPH Asia 2008 papers
User-assisted intrinsic images
ACM SIGGRAPH Asia 2009 papers
Correlation-based intrinsic image extraction from a single image
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Color lines: image specific color representation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
AppGen: interactive material modeling from a single image
Proceedings of the 2011 SIGGRAPH Asia Conference
Intrinsic images decomposition using a local and global sparse representation of reflectance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Intrinsic images using optimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Coherent intrinsic images from photo collections
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
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Decomposing an input image into its intrinsic shading and reflectance components is a long-standing ill-posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely-adopted Retinex-based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method. © 2012 Wiley Periodicals, Inc.