Lightness Recovery for Pictorial Surfaces
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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.
KeywordsLightness problem Color constancy Multispectral imaging Color correction Cultural heritage
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- Antonioli, G., Fermi, F., Oleari, C., & Riverberi, R. (2004). Spectrophotometric scanner for imaging of paintings and other works of art. In Proceedings of CGIV (pp. 219–224). Google Scholar
- Barrow, H. G., & Tenenbaum, J. M. (2008). Recovering intrinsic scene characteristics from images. In A. Hanson & E. Risenman (Eds.), Computer vision systems. San Diego: Academic Press. Google Scholar
- Bousseau, A., Paris, S., & Durand, F. (2009). User-assisted intrinsic images. In SIGGRAPH Asia. Google Scholar
- Castello del Buonconsiglio in Trento (Italy) (2006). http://new.buonconsiglio.it/index.php/en/.
- Farenzena, M., & Fusiello, A. (2007). Recovering intrinsic images using an illumination invariant image. In Proceedings of ICIP (Vol. 3, pp. 485–488). Google Scholar
- Forsyth, D. (2009). Light in space. Tech. rep., UIUC, IL. Google Scholar
- Funt, B., Drew, M., & Brockington, M. (1992a). Recovering shading from color images. In ECCV. Google Scholar
- Funt, B. V., Drew, M. S., & Brockington, M. (1992b). Reovering shading from color images. In Proceedings of ECCV, S. Margherita Ligure, Italy (pp. 124–132). Google Scholar
- Grosse, R., Johnson, M. K., Adelson, E. H., & Freeman, W. T. (2009). Ground truth dataset and baseline evaluations for intrinsic image algorithms. In ICCV. Google Scholar
- Lenz, R. (2002). Spaces of spectral distributions and their natural geometry. In Proceedings of CGIV, Poitiers, France (pp. 249–254). Google Scholar
- Mohen, J. P., Menu, M., & Mottin, B. (2006). Mona Lisa: inside the painting. New York: Harry N. Abrams Inc. Google Scholar
- Paviotti, A., Ratti, F., Poletto, L., & Cortelazzo, G. M. (2009). Multispectral acquisition of large-sized pictorial surfaces. EURASIP Journal on Image and Video Processing. Google Scholar
- Pelagotti, A., Mastio, A. D., & Razionale, A. V. (2007). Active and passive sensors for art works analysis and investigations. In SPIE conference series (Vol. 6491). Google Scholar
- Romeiro, F., Vasilyev, Y., & Zickler, T. (2008). Passive reflectometry. In Proceedings of ECCV (Vol. 4, pp. 859–872). Google Scholar
- Shen, L., Tan, P., & Lin, S. (2008). Intrinsic image decomposition with non-local texture cues. In CVPR. Google Scholar
- Tappen, M., Freeman, W., & Adelson, E. (2006). Estimating intrinsic component images using non-linear regression. In CVPR. Google Scholar
- Weiss, Y. (2001). Deriving intrinsic images from image sequences. In ICCV. Google Scholar