@ggbutcher Remember these charts?
And do not forget the CRI:
Have fun!
Claes in Lund, Sweden
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Was waiting for you to quote yourself. 
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I do remember them; your incandescent and halogen plots contributed to my thinking that a generic profile of a lighting category would be sufficient. Still not off that, as having to include a spectrometer in the lashup will daunt a lot of folk. I am going to acquire an i1Studio, @JackH helped me cross the utility threshold. But, until it just doesn’t make sense, I’ll use that device to assess the feasibility of generic “category” profiles, at least for tungsten and halogen sources.
I so regret missing when they had that on sale (I think around $100 discount?) a year or two ago by just one day.
Edit: Refurbs on eBay are pretty well priced, around $175… I think I’m gonna grab one of those.
Edit 2: Wait, maybe not. xRite’s product naming is crap. i1Display Studio vs. i1Studio. i1Studio is $350, so not sure if worth the risk of a refurb product.
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Those sky spectrum plots have me thinking… could we use the sun/sky as light source for SSF calibration? Or is the spectrum too variable depending on latitude/date/time of day? Or not intense enough (would require mirrors or lenses)?
It would be an excellent light source for profiling, but it’s kinda hard to channel into an optical setup. You’re also at the mercy of the weather, even clouds and haze. Right now in Colorado Springs, we’ve only had maybe two days in the past month where the sky was almost completely unencumbered of clouds, or forest fire haze…
The sun might burn a hole in your sensor. Oh yes, those fires. My cousin from the west (BC, Canada) says she hasn’t been able to see the sunset. She has an autoimmune disease, so it has been tough on her.
Apparently the smoke has now gotten high enough into the stratosphere to hit some eastward winds that are blowing it all the way over to the East Coast.
Yes, even OUR skies are getting hazy and reddish now!
Edit: Which makes a perfect use case for SSF measurement since we’re losing easy access to clean D50.
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Thanks for checking out the grating corrections!
It’s a bit disappointing it didn’t improve things and also slightly surprising, given the rather large correction (factor 2 over the range of interest). At the same time I don’t know what exactly goes into the DE calculation, so it’s totally imaginable that these smooth, continuous corrections have not that high an impact.
Anyway regardless of potentially open questions, the conclusion is nice: The cheap grating is just fine for good results 
I still haven’t exonerated my calibration operator in ssftool; it’s a simple division, but along with 1.0 normalization I’m not sure it’s all acting correctly.
That would be ssf_powercalibrate? And calibrationfile is something like a normalized version of source powerspectrum * grid spectral efficiency * diffusor power efficiency? That looks quite straightforwardly correct to me (though not too much stock should be given to such a statement from me ). Or what is there concretely that makes you doubt it’s correctness?
I don’t know yet, just looking at some normalized data out of it just didn’t look like the relationship was preserved. It’s something I’m going to pick at next week…
Now, how you express it, as a product of the three, I haven’t done; I’ve attempted to apply each individually to the SSF measurements. I don’t see how that’d yield a different result, but I’m not really a math guy and a lot of those implications escape me until I just go and try them… 
This just hit hackaday:
There’s some good discussion of calibration sources in the comments.
Most notably https://eprints.lancs.ac.uk/id/eprint/6736/1/inproc_326.pdf is linked from the comments
There’s a bit of discussion regarding line CCD vs monochrome 2D camera, TBH for our purposes a monochrome 2D camera is fine for the “reference” calibration, and obviously for the camera to be characterized itself, it has to be a non-monochrome camera
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Interesting line CCD monochrome sensor. I wonder if they compensate the CCD for angular dependence and QE in their project:
Should be relatively easy to drive directly from an ESP32 or similar via i2c? No need for a lens in our application I think.
Or from a Pi, although in this application:
Since the camera we’re measuring has a 2D sensor, most of the benefits of line sensors are lost.
The main thing there that was useful for this application is the discussion of calibration sources. Specifically that paper discussing using “consumer” tungsten bulbs for spectral reference.
In this case, we’re interested in the camera’s SSF, so we need to know:
- The SSF of our reference sensor (this could be hard without existing data, but perhaps for a monochrome sensor, the general SSF of silicon CMOS is close enough?)
- The spectral power distribution of our light source
- This lets us figure out the SSF of our optical system, which can be used to compensate it out when measuring
- The SSF of our actual camera
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Though it being a single line sensor is not a problem if we use it as a calibrator, assuming we trusted the QE curve in the spec sheet:
- Place inline sensor in box, turn on well behaved source (knowledge of SPD not necessary) and take picture of spectrum from grating with it
- Compensate for inline sensor QE = spectral photon distribution of source signal after grating
- Place camera to be tested in box, turn on the same source and take picture of spectrum from grating
- Normalize with the spectral photon distribution from 2) = CFA SSFs
There are much cheaper such inline sensors out there, such as this < $5 from Toshiba. I think they are used in scanners and the like. None of them include an onboard ADC (nor does the hackaday one) so an ADC-less Pi is probably less than optimal.
The key would be finding one whose ‘typical QE’ plot is repeatable and trustworthy. Do they exist?
This one appears to be spectral energy so it would need to be converted to quanta. And it obviously includes a coverglass. I wonder why they don’t show the response below 400nm.
I think you have replaced ‘estimate spectral photon flux from similar enough lightsource’ with ‘estimate spectral photon flux from similar enough sensor’.
Nothing wrong with that, it takes out the optical system in between the actual sensor and the lightsource. But it also relies on the published QE of the line sensor with some assumptions. Specifically any angular dependence of the line-sensor-QE or the actual-sensor-QE will lead to minor problems as well as line-sensor-QEs variance. Spatial ADC gain variances of both sensors not even touched.
At that point a calibrated photodiode that one scans through the image plane connected to a good enough voltmeter might make more sense.
The question is, which way forward is practical and might improve on the existing results?
Ha, buy an i1Studio! I know when I’m out of my league… 
I had the money set aside, then decided to upgrade my computer instead. Now, I don’t have to take vacation days to run nlmeans denoise (another technical bit where I’m out of my league), but I have to start socking away my allowance again for the xRite spectrometer. Actually though, the 1-line arrays present a decent electronics project, something where I am in my league…
Seriously, i do need to pick away at these smaller influences. Still, when I went to compare results for my D7000 with a larger training set (Munsell spectra set in dcamprof), the max DE difference was even smaller, 4.61 for a LUT profile based on my measurement vs. 4.33 for the rawtoaces monochromator-measured equivalent. I’m using my Z 6 profile from my Rube Goldberg spectrscope data to good effect now. What’s that old saw about perfect being the enemy of good enough?
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Yes indeed, hence the skepticism at the end. My feeling is that variance in the latter is substantially less than in the former, especially because of the difficulty in controlling DIY temperatures and drive currents inexpensively.
A chunky slice of the budget of the Spectron II went to stabilizing the source and the integrator. On the other hand I suspect that the QE curve of the inline sensor should be repeatable with decent accuracy, at least from similar batches.
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