High Quality Color Restoration using Spectral Power Distribuion for 3 D Textured Model

In connection with e fforts to preserve objects of cultural heritage, we are attempting to recreate the real color of a wall painting in the ancient Ozuka tumulus. The di fficulties are that there is no sunlight in the tumulus, and we are not allowed to use torchlight because of preservation considerations. So to observe a wall painting under these conditions, we have to observe a computation image. We have found that for high quality color restoration and color preservation, we can use spectral power distribution. We have discovered that a color signal spectral is suitable for color preservation because it has more color data than RGB three-channel data, and it does not depend on a measuring implement. When we use spectral power distribution, we can create images that have more precise colors that those created using RGB data. But the spectral power distribution data that we can acquire has low resolution. So by matching a high-resolution RGB image and a low-resolution spectral power distribution image, we obtain a restored image that has high-resolution and high-quality color.

[1]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .

[2]  Mark S. Drew,et al.  Separating a Color Signal into Illumination and Surface Reflectance Components: Theory and Applications , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Shoji Tominaga,et al.  Surface Identification Using the Dichromatic Reflection Model , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Po-Rong Chang,et al.  Constrained nonlinear optimization approaches to color-signal separation , 1995, IEEE Trans. Image Process..

[5]  G D Finlayson,et al.  Color constancy at a pixel. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Katsushi Ikeuchi,et al.  Simultaneous 2D images and 3D geometric model registration for texture mapping utilizing reflectance attribute , 2002 .

[7]  Shree K. Nayar,et al.  Generalized Mosaicing: Wide Field of View Multispectral Imaging , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  J. Marchant,et al.  Spectral invariance under daylight illumination changes. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  Katsushi Ikeuchi,et al.  Illumination chromaticity estimation using inverse-intensity chromaticity space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Katsushi Ikeuchi,et al.  Color alignment in texture mapping of images under point light source and general lighting condition , 2004, CVPR 2004.

[11]  Tomohito MASUDA Sunlight Illumination Simulation for Archaeological Investigation-Case Study of the Fugoppe Cave - , 2004 .

[12]  Katsushi Ikeuchi,et al.  Mapping textures on 3D geometric model using reflectance image , 2005, Systems and Computers in Japan.

[13]  Atsushi Nakazawa,et al.  Fast simultaneous alignment of multiple range images using index images , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).