To show the effects of pixel fill factor on aliasing, I coded up a
small simulation. This 1000x1000 starting image:

https://dl.dropboxusercontent.com/u/20239870/Aliasing/micro-auto-focus-test-2.png
(from http://www.komar.org/faq/camera/auto-focus-test/ )

represents the image falling onto a 111x111 monochromatic pixel
sensor. Since each sensor pixel sees 9x9 pixels of the original image,
we can create 9x9 downsampling kernels to simulate various sensor
pixel fill factors.

Here is the resulting image from a kernel using only a 1x1 portion of
the 9x9 kernel (1% fill factor, approximating "instantaneous" or
infinitesimal point sampling):
https://dl.dropboxusercontent.com/u/20239870/Aliasing/kernel_1.png

A 3x3 kernel (11% fill factor):
https://dl.dropboxusercontent.com/u/20239870/Aliasing/kernel_3.png

A 5x5 kernel (31% fill factor):
https://dl.dropboxusercontent.com/u/20239870/Aliasing/kernel_5.png

A 7x7 kernel (61% fill factor):
https://dl.dropboxusercontent.com/u/20239870/Aliasing/kernel_7.png

A 9x9 kernel (100% fill factor--no insensitive gap between pixels):
https://dl.dropboxusercontent.com/u/20239870/Aliasing/kernel_9.png

And finally the code:
https://dl.dropboxusercontent.com/u/20239870/Aliasing/FillFactor.py

You can see that the sensor pixel fill factor has a considerable
effect on the amount of aliasing in the output image.

On Wed, Oct 9, 2013 at 12:08 PM, Larry Colen <l...@red4est.com> wrote:
> It seems to me that if you had a rear illuminated sensor, with no space
> between the pixels, and it had no bayer filter, then aliasing/moire would
> not happen, because the light value would be averaged over the whole sample.
>
> It's the discontinuous aspect of what is effectively three overlayed photos
> that is causing the aliasing.
>
> Is this correct?
>
> --
> Larry Colen                  l...@red4est.com         http://red4est.com/lrc
>
>
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