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Making Foveon-like Images with a Bayer Camera

Started Feb 23, 2022 | Discussions
Tom Schum
Tom Schum Forum Pro • Posts: 13,282
Re: Making Foveon-like Images with a Bayer Camera

k_db wrote:

the browns look relatively distinct from the yellows but obviously it’s hard to tell as just a viewer. did you reduce by 25% when doing the bilinear move? i think that’s the important part for the pixels to average together correctly!

Yes, when I downsized, I divided the width number by two.  When I put that into the resizing tool, the height number was automaticaly modified.  Four of the original pixels then became one pixel in the result.

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Tom Schum
"Beware of taking advice from anonymous wise men." Quote from Anon.

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k_db Junior Member • Posts: 33
Re: Making Foveon-like Images with a Bayer Camera

nice 👍

joe173 Contributing Member • Posts: 590
Re: Making Foveon-like Images with a Bayer Camera
1

xpatUSA wrote:

NordicFlyer wrote:

Brand loyalty means very little to me - a camera is simply a tool.

I second that.

I was loyal to the basic principle of the 1:1:1 Foveon because I didn't like CFA demosaicing guesswork by interpolation and other wizardry.

Any loyalty I had to Sigma themselves ceased with the introduction of the Quattro series and the fp models.

Meh..the Quattro never did "interpolation". It correlated the lost data from other layers as explained. It turns out the original sensor captured a lot of redundant information at the expense of image noise, more than it needed.

https://www.imaging-resource.com/news/2014/04/08/sigma-qa-part-ii-does-foveons-quattro-sensor-really-outresolve-conventional

SR: You can see that the top layer is blue-heavy, but it's not blue. The next layer is green-heavy, but it isn't green. The bottom is red-heavy. None of these is just red, green, or blue -- that really allows you to do something very interesting. So this is fundamentally a pretty smart way to keep your information, but at the same time, reduce the whole load on the system, because these things are not pure colors. It may sound counterintuitive, but it actually allows you to separate out very cleanly all the detail information -- as we call it -- from the top layer, and understand where the color detail comes from. In other words, it allows us to actually get back what was apparently lost.

DE: I see. Because each layer has all the colors, you can correlate. So you can look at the top layer and say "Okay, we know we've got some red here," then you look at the red layer to see how much is there, and you can sort of take that out.

SR: Precisely -- it creates the correlation automatically, and therefore, you can remove some redundant information that you didn't actually need. Part of the advantage that you get from that is that you are then able to increase the signal.

DE: That's an interesting concept. You're taking advantage of redundancy in the signal to simplify the processing, and also help with the noise levels.

SR: Which is why the spectral response is so important -- because it tells you there is actually more information than just the spatial, three-dimensional structure to work from. If you utilize that additional information...

DE: Yeah, it's not like you just have a blue filter on top of everything.

SR: It would be impossible then, without that correlation, to do this.

DE: That's very, very interesting. There has to be some kind of trade-off -- nothing comes for free. Is there likely to be any less resolution in the green and red at all? I wouldn't think you would have color aliasing, but I wonder what the consequence would be?

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