Practical Spectral Photography

1. Practical Spectral Photography Ralf Habel1 Michael Kudenov2 Michael Wimmer1 Institute of Computer Graphics and AlgorithmsVienna University of Technology1 Optical Detection LabUniversity of Arizona2

2. Motivation • Spectroscopy is most important analysis tool in all natural sciences • Astrophysics, chemical/material sciences, biomedicine, geophysics,… • Industry applications: • Mining, airborne sensing, QA,… • In computer graphics: • Colors • Material reflectance • Spectral/predictive rendering • … Ralf Habel

3. Spectral Imaging • Records image at narrow wavelength bands • In visible range not only RGB (3 channels)but many more (6-400 channels) • Result: 3D data cube • 2 spatial image axis • 1 wavelength axis Ralf Habel

4. Spectral Imaging • Usually done with highly specialized devices • Many methods to build devices • Scanning slits, rotating mirrors, special sensor, filters, prisms, … • Usually scan along one of the data cube axis • All very costly due to opto-mechanical components • “Simplest” spectral imager: • Camera + band filters • Requires switching of filters • Limited in number of bands Ralf Habel

5. Motivation • Why not use consumer cameras and equipment for spectral imaging? • High quality, very sensitive • Highly accurate lenses • Practical Constraints: • No camera modification • No lab/desktop/optical bench setup • No expensive components Ralf Habel

6. CTIS Principle • Computed Tomography Image Spectrometer • Diffraction grating parallel-projects 3D data cube in different directions on image plane (sensor): Ralf Habel

7. CTIS Principle • Computed Tomography Image Spectrometer • Diffraction grating parallel-projects 3D data cube in different directions on image plane (sensor): Ralf Habel

8. CTIS Principle • Sensor records projections of 3D data cube • All information needed is recorded in one image • “Snapshot” spectrometry • Challenge is to reconstruct 3Ddata cube from projections • Tomographic rec. with ExpectationMaximization • More details in paper Ralf Habel

9. CTIS Optical Path Imaging lens + square/slit aperture creates virtual image Ralf Habel

10. CTIS Optical Path Imaging lens + square/slit aperture creates virtual image Collimating lens makes light parallel Ralf Habel

11. CTIS Optical Path Imaging lens + square/slit aperture creates virtual image Collimating lens makes light parallel Diffraction grating creates projections Ralf Habel

12. CTIS Optical Path Imaging lens + square/slit aperture creates virtual image Collimating lens makes light parallel Diffraction grating creates projections Re-imaging lens focuses on sensor Ralf Habel

13. CTIS Optical Path Imaging lens + square/slit aperture creates virtual image Collimating lens makes light parallel Diffraction grating creates projections Re-imaging lens focuses on sensor Ralf Habel

14. CTIS Optical Path • Built with: • Drain pipe & duct tape • 50mm, 17-40mm and macro lens • Diffraction gel ($2 per sheet) in gel holder Ralf Habel

15. CTIS Camera Objective Ralf Habel

16. CTIS Camera Objective Ralf Habel

17. HDR Image Acquisition No overexposed pixels allowed Projections(diffractions) weaker than center image Avoids noisy signal where camera response is weak Ralf Habel

18. Spatial Wavelength Calibration • Mapping from 3D data cube into projections • Laser pointers (red, green and blue) with known wavelengths shot through a diffusor and pinhole • Monochromatic point light source • Pictures of pinhole give mapping of one voxel in 3D data cube • All other projections valuesinterpolated/extrapolated Ralf Habel

19. CTIS Principle Ralf Habel

20. Spatial Wavelength Calibration Ralf Habel

21. Spectral Response Calibration • Spectral response of the diffraction grating + RGB sensor for red, green and blue • Picture of light source with continuous known spectrum • We use calibrated halogen lamp Ralf Habel

22. Spectral Photography Results • Take HDR picture with CTIS camera objective • Reconstruct 3D data cube for red, green and blue image color channels • Mapping from spatial calibration • Combine RGB spectral response of each pixel to true spectrum with spectralde-mosaicking • Mapping from spectral response calibration Ralf Habel

23. Spectral Photography Results Protoype data cube resolutions:120x120 pixels4.59 nm (54 channels) Accuracy reduced in high blue and low reds dueto color filters Slight Expectation Maximization reconstruction artifacts Nowhere near possible optimum! Ralf Habel

24. Spectral Photography Results Ralf Habel

25. Spectral Photography Results Ralf Habel

26. Future • Better CTIS objective • Drain pipes and duct tape have their limits… • Optimized optical path and components • More compact/integrated device • Increase data cube resolution/accuracy: • Structured aperture • Digital holography – form diffraction/projections in any way • Better solutions to tomographic reconstruction • Is active research in optics • No vision based approach yet! Ralf Habel

27. Future • Turning mobile devices into spectrometers - consumer spectroscopy? • 8 MP high sensitivity sensors • HDR capabilities • Very low cost! • “Snapshot” capability: Spectral movies with consumer cameras? • Not only good for computer graphics: • Blood sample analysis • Water contamination analysis • As part of a TricorderTM Ralf Habel

28. Practical Spectral Photography Thank You! Ralf Habel