Pattern Recognition Letters
Direct methods in the calculus of variations
Direct methods in the calculus of variations
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Color by Correlation: A Simple, Unifying Framework for Color Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An affordable multispectral imaging system for the digital museum
International Journal on Digital Libraries - Special section on Digital Museum
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
A System for 3D Modeling Frescoed Historical Buildings with Multispectral Texture Information
Machine Vision and Applications
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
User-assisted intrinsic images
ACM SIGGRAPH Asia 2009 papers
Multispectral acquisition of large-sized pictorial surfaces
Journal on Image and Video Processing
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An important technique in cultural heritage preservation is multispectral acquisition, where one recovers a detailed spectral record of a painting using carefully calibrated lighting. This is difficult to do with frescoes, because it is hard to recover the spatial variation in light intensity that results from factors like the imaging setup and the curvature of the fresco. We introduce a new formulation of the lightness problem applied to images of pictorial artworks. The problem is different from the conventional lightness problem, because artists often paint the effects of light, so the albedo field contains a component that mimics an illumination field. Our method distinguishes between physical illumination and painted shading through spatial frequency effects and dynamic range considerations. We evaluate our method using multispectral images of paintings, where the physical illumination field is known. Our method produces estimates of the illumination intensity field that compare very well with the known ground truth, and outperforms other state-of-the art lightness recovery algorithms. For frescoes, ground truth is not available, but we show that our method produces consistent results, in the sense that the illumination functions estimated on the image and on (some of) its subimages are very similar on the overlap. We show our method produces qualitatively good color corrections for images of frescoes found on the web.