Research question: Can image processing /computer vision be used to increase the free spectral range (or reduce spectral overlap) of spectrometer designs?
Background:Many spectrometer designs are limited by the ‘spectral overlap’ problem which occurs when light spills over from one order to another. The general approach to solve the spectral overlap problem is to use an order sorting filter that blocks unwanted light but also reduces the overall spectral range. My research question is whether a software version of a blocking filter could quantify and compensate the spectral overlap. Please see http://www.vikdhillon.staff.shef.ac.uk/teaching/phy217/instruments/phy217_inst_fsr.htmlfor more info.
Spectral overlap demonstration: In order to demonstrate spectral overlap impact, a slitless boxless imaging spectrograph was created using a Raspberry PI NoIR V2 camera. Schematic below shows the camera setup consisting of a NoIR camera with a 500 line/mm transmission grating that receives light from a vertical row of different color Light Emitting Diodes (LEDs).
This goal of the design is to create a spectrum staircase effect that permits observations of multiple light orders diffracted across the NoIR camera. List of light sources (from top to bottom) and significant spectral features are listed below: -1)Red Laser diode (~650nm) - Spectral marker with narrow output. -2)IR LED (~940 nm) –Marks edge of NoIR camera spectral range -3)IR LED (~850nm) – Center Near Infrared Band -4)White light LED – High intensity to observe second order spectrum -5)RED Laser Diode – Laser diode (used primarily to keep the staircase jumps even) -6)UV/Blue LED/White paper – Same LED as #7 but with white paper that creates green florescence spectra. -7)UV/Blue LED –Marks edge of NoIR camera spectral range The pictures below shows typical images from the spectrograph and demonstrate several types of spectral overlap. For example, blue LED second order overlaps with first order of IR LEDs, bright white LED overlaps with IR LEDs and blue/UV LED overlaps with paper fluorescence spectra.
Initial review:
The following pictures display 3d intensity profiles for the RGB components of the spectral staircase spectrograph example above. The goal of the 3D intensity profiles is to provide insight into how computer vision ‘sees’ the spectral images. The hope is that the profiles will help develop algorithms for a software order sorting filter.
Since the full array 3d profiles are quite complex, the white light 2nd order spectrum was extracted. The picture below shows the Blue/Green/Red ‘rolling hill’ pattern typical of white light spectrum.
Code used for image/ capture display and staircase example spectrograph is available at: https://github.com/MargaretAN9/Peggy
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3 Comments
@maggpi, this is definitely a relevant parameter to explore, what I'm missing here are a few equations which describe the relation between the line density, illuminated area, and wavelength. Take a look here as a starting point: http://hyperphysics.phy-astr.gsu.edu/hbase/phyopt/grating.html I'm not sure I got exactly how you set up your system, how sure are you that all LEDs are aligned? The 850, 950nm LEDs appear to be saturated. When saturation occurs photo-electrons can spill to pixels along the same line/column known as Blooming (I'm not sure it's relevant for the Rpi camera thou). Possibly try to capture an image of a CFL lamp, while it doesn't have many features in the IR, you should be able to extract a clear calibration, and if you can set the exposure time to calibrate the second order as well you could get some real understanding on that overlap. Another interesting approach would be to look at the RGB values: keep in mind that IR is hardly discernable between the RGB, while the second order will be most visible in the B channel. try looking there!
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You probably noticed by now that even a thing as simple as RGB2GRAY cand be made in different ways, look for rpi camera debayer to read more on this topic!
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