Spectral modeling of reflective-fluorescent Scenes

The spectral reflectance of objects provides innate information about material properties that have proven useful in applications such as classification, synthetic relighting, and medical imaging to name a few. In recent years, fluorescence analysis of scenes has received attention. Fluorescence emission is a common phenomenon occurring in many objects from natural gems and corals, to fluorescent dyes used for clothing; even laundry detergent contains fluorescence dyes to brighten/whiten clothing. What makes fluorescence different from ordinary reflection is the transfer of energy from one wavelength to another. More specifically, fluorescent materials absorb light at a certain wavelength and then reemit it at other wavelengths after, whereas ordinary reflective components reflect light at the same wavelength as incident. We demonstrate efficient separation and recovery of reflective and fluorescent emission spectra through the use of high frequency illumination in the spectral domain. We also achieve this separation task by using a single hyperspectral image. After identifying the critical hurdle in single-image component separation, we mathematically design the optimal illumination spectrum, which is shown to contain substantial high-frequency components in the frequency domain. This observation, in turn, leads us to recognize a key difference between reflectance and fluorescence in response to the frequency modulation effect of illumination, which fundamentally explains the feasibility of our method. On the practical side, we successfully find an off-the-shelf lamp as the light source, which is strong in irradiance intensity and less expensive. Experiments using both synthetic data and real images have confirmed the validity of the selected illuminant and the accuracy of our separation algorithm. We also show that fluorescence can provide additional information about scenes and demonstrate new applications using fluorescence of real objects such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting