How sensor spectral sensitivity curves affect color and image rendering

Asked 9/28/2013

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I’m trying to understand what a camera sensor’s spectral sensitivity curves mean for the final image. If one camera’s RGB channels have broad, overlapping sensitivity curves, while another camera’s channels are narrower and more separated, how does that change the recorded colors? Am I right to think broad overlap means each channel responds to a wider range of wavelengths, while narrow curves mean each channel is concentrated in a smaller range? What practical effect would these different sensitivity shapes have on an image?

Originally by Photography Stack Exchange contributor. Source · Licensed CC BY-SA 4.0

Photography Stack Exchange contributor

12y ago

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The subject is much more complex than the following "simplistic layman's answer"** , but ...

Simplistically, a sample of light at a point can be represented by a two dimensional position plus an amplitude, or by the amplitude of three orthogonal component vectors. It is traditional to use Red Green and Blue, approximately corresponding to the colour receptor wavelengths in most human eyes* but other component vectors could equally be used. (* Some eyes lack all or some sensitivity in one or more receptors and it is claimed by some that a few people have an additional wavelength receptor).

The "red" curve set lacks sensitivity on some areas - overall light measured would be less than the total.

The "blue" curve set overlaps very significantly. Light at a wavelength midway between the Red (right hand) and Blue (middle) pek response points would appear equally in both red and green channels and could not be distinguished in any one channel from monochromatic light of a slightly lower intensity.

I would expect that the narrow red curves would have some issues with low sensitivity and not dealing well with some light with distinct spectra peaks BUT
I would expect the wide blue curves to provide a munged pastel colour inaccurate mess.

Of the two I would expect the narrow curves to do better, but better still would be something with broader squarer non overlapping response curves which have both very little dead space and minimal overlap. Non-existeum selective interference filters would probably meet the need.

Many idea starters and examples here

What real people typically claim From here

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BUT there are many variations depending on application. One "trick" is to use sharp squarush non overlapping filters plus a luminance channel that covers the whole spectrum.

A few other examples.

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** Whether simplistic-answer or simplistic-layman left to reader's discretion.

Originally by user6263. Source · Licensed CC BY-SA 4.0

user6263

12y ago

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AI Answer

Generated from our catalog & community — verify before relying on it.

Yes—broad, overlapping curves mean each RGB channel responds to a wider range of wavelengths, while narrower curves respond more selectively. The main effect is color separation.

With broad overlap, a wavelength that looks “mostly green” may also raise the red and blue channels. That channel crosstalk can reduce color discrimination, so cameras rely more on color processing/profiles to produce accurate-looking color.

With narrower, better-separated curves, each channel is more selective, which can improve color separation for technical measurement. But no real camera has perfectly isolated RGB bands, and very narrow curves are not automatically “better” for normal photography.

So the graph is not about brightness being “uniform around RGB.” It shows how strongly each channel responds across wavelength. From that you can infer:

  • overlap = more shared response between channels
  • separation = less crosstalk
  • gaps or weak regions = reduced sensitivity there

In practice, the final image depends heavily on the camera’s color calibration and processing. Manufacturers measure sensor response and build profiles so JPEGs and raw conversions map those sensor signals into pleasing or accurate colors.

UniqueBot

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12y ago

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