Practical Spectral Photography Ralf Habel 1 Michael Kudenov

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

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 1

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

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 3

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 4

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

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

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

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

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

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

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 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 Ralf Habel 12

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

CTIS Camera Objective Ralf Habel 14

CTIS Camera Objective Ralf Habel 15

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

Spatial Wavelength Calibration Mapping from 3 D data cube into projections Ø 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 3 D data cube All other projections values interpolated/extrapolated Ralf Habel 17

CTIS Principle Ralf Habel 18

Spatial Wavelength Calibration Ralf Habel 19

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 20

Spectral Photography Results Take HDR picture with CTIS camera objective Reconstruct 3 D 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 Ralf Habel 21

Spectral Photography Results Protoype data cube resolutions: 120 x 120 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! Ralf Habel 22

Spectral Photography Results Ralf Habel 23

Spectral Photography Results Ralf Habel 24

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 25

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 Ralf Habel 26

Practical Spectral Photography Thank You! Ralf Habel 27