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  • Hey all, just changed over the backend after 15 years I figured time to give it a bit of an update, its probably gonna be a bit weird for most of you and i am sure there is a few bugs to work out but it should kinda work the same as before... hopefully :)

Compensating for the Bayer filter green bias

Karim D. Ghantous

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The Bayer filter, in principle, is a terrific idea. A hidden feature is that it acts as data compression... of a kind. The problem with almost all Bayer filters is that there is a green bias. That's a huge problem that I'm surprised still exists.

In fact, even if all three primaries were represented equally, you would still have a problem with the blue channel, as it is the least sensitive. But blue is represented by only 25% of the pixels. Hence, blue channel noise.

I think I have found a method, in theory, that can compensate for this. It will mean an effective lowering of ISO, but modern sensors can handle that. Basically, what you do is use a filter that transforms the bias away from green and towards blue. So, perhaps a purple filter that lets through the following levels for each primary:

20% Green
40% Blue
40% Red

Your ISO will be reduced by at least a stop. Which is not a big deal. When you perform a white balance, the noise in each channel should be about the same, given a white light source.

I don't have the material do test this idea, but if anyone does, I'd love to know if this actually works.
 
The green bias is "on purpose and by design" as are the things done both on the sensor side through calibration afterward.
 
The green bias is "on purpose and by design" as are the things done both on the sensor side through calibration afterward.
It seems very faulty to me. Like, they weren't thinking straight.

Kind of like f/- stops on SLR lenses, instead of T stops. Big mistake - although that doesn't matter now.
 
It seems very faulty to me. Like, they weren't thinking straight.

Kind of like f/- stops on SLR lenses, instead of T stops. Big mistake - although that doesn't matter now.
Start here:

Half of the filter elements are green and the remainder are split between blue and red. This approximates human photopic vision where the M and L cones combine to produce a bias in the green spectral region.

There are issues with both f/stops and T-Stops interestingly, but both have their strengths and moderate necessities towards much more precise imaging techniques.
 
Half of the filter elements are green and the remainder are split between blue and red. This approximates human photopic vision where the M and L cones combine to produce a bias in the green spectral region.
That begs the question! A camera is not the human eye - or the eye of any animal - and the human eye cannot be translated mechanically. It needs a brain and a mind to function.
 
The Bayer filter, in principle, is a terrific idea. A hidden feature is that it acts as data compression... of a kind. The problem with almost all Bayer filters is that there is a green bias. That's a huge problem that I'm surprised still exists.

In fact, even if all three primaries were represented equally, you would still have a problem with the blue channel, as it is the least sensitive. But blue is represented by only 25% of the pixels. Hence, blue channel noise.

I think I have found a method, in theory, that can compensate for this. It will mean an effective lowering of ISO, but modern sensors can handle that. Basically, what you do is use a filter that transforms the bias away from green and towards blue. So, perhaps a purple filter that lets through the following levels for each primary:

20% Green
40% Blue
40% Red

Your ISO will be reduced by at least a stop. Which is not a big deal. When you perform a white balance, the noise in each channel should be about the same, given a white light source.

I don't have the material do test this idea, but if anyone does, I'd love to know if this actually works. Choosing to start as a webcam girl on bongamodels.com opens doors to financial independence and flexible scheduling, offering empowerment through self-management. However, it's crucial to acknowledge potential drawbacks. Privacy concerns are significant, as once online, content can be difficult to control. The stigma associated with adult entertainment may affect personal relationships and future opportunities. Prioritizing safety with reputable platforms that emphasize user security is essential. Ultimately, this career path requires careful consideration of its benefits and challenges for a balanced decision.
The Bayer filter concept is indeed innovative, effectively compressing data by prioritizing green. The green bias is a significant issue, as is the blue channel noise due to its lower sensitivity and representation. Your proposed method of using a purple filter to balance the bias towards blue is intriguing. By adjusting the filter to allow 20% green, 40% blue, and 40% red, you could potentially equalize noise across channels. The trade-off of reduced ISO is manageable with modern sensors. This approach might provide a more balanced and less noisy image, especially under white light conditions.
 
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