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Practical Spectral Photography Ralf Habel 1 Michael Kudenov 2 Michael Wimmer 1 Institute of Computer Graphics and Algorithms Vienna University of Technology.

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Presentation on theme: "Practical Spectral Photography Ralf Habel 1 Michael Kudenov 2 Michael Wimmer 1 Institute of Computer Graphics and Algorithms Vienna University of Technology."— Presentation transcript:

1 Practical Spectral Photography Ralf Habel 1 Michael Kudenov 2 Michael Wimmer 1 Institute of Computer Graphics and Algorithms Vienna University of Technology 1 Optical Detection Lab University of Arizona 2

2 Ralf Habel 1 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 …

3 Ralf Habel 2 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

4 Ralf Habel 3 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

5 Ralf Habel 4 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

6 Ralf Habel 5 CTIS Principle Computed Tomography Image Spectrometer Diffraction grating parallel-projects 3D data cube in different directions on image plane (sensor):

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

8 Sensor records projections of 3D data cube All information needed is recorded in one image “Snapshot” spectrometry Challenge is to reconstruct 3D data cube from projections Tomographic rec. with Expectation Maximization More details in paper Ralf Habel 7 CTIS Principle

9 Ralf Habel 8 CTIS Optical Path Imaging lens + square/slit aperture creates virtual image

10 Ralf Habel 9 CTIS Optical Path Imaging lens + square/slit aperture creates virtual image Collimating lens makes light parallel

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

12 Ralf Habel 11 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

13 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

14 Ralf Habel 13 CTIS Optical Path Built with: Drain pipe & duct tape 50mm, 17-40mm and macro lens Diffraction gel ($2 per sheet) in gel holder

15 Ralf Habel 14 CTIS Camera Objective

16 Ralf Habel 15 CTIS Camera Objective

17 Ralf Habel 16 HDR Image Acquisition No overexposed pixels allowed Projections (diffractions) weaker than center image Avoids noisy signal where camera response is weak

18 Ralf Habel 17 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 values interpolated/extrapolated

19 Ralf Habel 18 CTIS Principle

20 Ralf Habel 19 Spatial Wavelength Calibration

21 Ralf Habel 20 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

22 Ralf Habel 21 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 spectral de-mosaicking Mapping from spectral response calibration

23 Ralf Habel 22 Spectral Photography Results Protoype data cube resolutions: 120x120 pixels 4.59 nm (54 channels) Accuracy reduced in high blue and low reds due to color filters Slight Expectation Maximization reconstruction artifacts Nowhere near possible optimum!

24 Ralf Habel 23 Spectral Photography Results

25 Ralf Habel 24 Spectral Photography Results

26 Ralf Habel 25 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!

27 Ralf Habel 26 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 Tricorder TM

28 Ralf Habel 27 Practical Spectral Photography Thank You!


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