Reflectance and Fluorescent Spectra Recovery Based on Fluorescent Chromaticity Invariance under Varying Illumination

In recent years, fluorescence analysis of scenes has received attention. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescence emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescence emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

[1]  Hans-Peter Seidel,et al.  Acquisition and analysis of bispectral bidirectional reflectance and reradiation distribution functions , 2010, SIGGRAPH 2010.

[2]  Imari Sato,et al.  Spectral Modeling and Relighting of Reflective-Fluorescent Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  David J. Kriegman,et al.  Shape from Fluorescence , 2012, ECCV.

[4]  Kobus Barnard Color Constancy with Fluorescent Surfaces , 1999, Color Imaging Conference.

[5]  Shree K. Nayar,et al.  Multispectral Imaging Using Multiplexed Illumination , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  Amit Gupta,et al.  Spectral imaging microscopy web sites and data , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[7]  Takahiko Horiuchi,et al.  Spectral Estimation of Fluorescent Objects Using Visible Lights and an Imaging Device , 2011, Color Imaging Conference.

[8]  Werner Purgathofer,et al.  A reflectance model for diffuse fluorescent surfaces , 2006, GRAPHITE '06.

[9]  Jinwei Gu,et al.  Recovering spectral reflectance under commonly available lighting conditions , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Imari Sato,et al.  Separating reflective and fluorescent components of an image , 2011, CVPR 2011.

[11]  Mark D. Fairchild,et al.  Full-Spectral Color Calculations in Realistic Image Synthesis , 1999, IEEE Computer Graphics and Applications.

[12]  Takahiro Okabe,et al.  Bispectral photometric stereo based on fluorescence , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Jan Kautz,et al.  Acquisition and analysis of bispectral bidirectional reflectance and reradiation distribution functions , 2009 .

[14]  Feng Xiao,et al.  Illuminating Illumination , 2001, CIC.

[15]  Takahiro Okabe,et al.  Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  James E. Leland,et al.  Principles of bispectral fluorescence colorimetry , 1997, Optics & Photonics.

[17]  H. Seidel,et al.  Fluorescent immersion range scanning , 2008, ACM Trans. Graph..

[18]  Yoav Y. Schechner,et al.  Multiplexed fluorescence unmixing , 2010, 2010 IEEE International Conference on Computational Photography (ICCP).

[19]  J. Parkkinen,et al.  Characteristic spectra of Munsell colors , 1989 .

[20]  Takahiro Okabe,et al.  Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance , 1987 .