# Cubic interpolation (or: how to get the filter matrix?)

So I’ve been trying to wrap my head around this particular piece of information:

I understand what h_l^T h_l and h_c^T h_c do and how to apply the resulting matrices. However, I do not understand how the author arrived at the ‘unseparable’ matrices in (3.6). Reading the references paper [32] did not help too much.

Is anyone here who can clear up the confusion, and possibly show me how this could be extended to even larger filters (just for fun)?

@Thanatomanic Roel, is the 7x7 kernel in 3.6) meant to be for the green channel of a bayer array?
If that’s the case, It looks wrong… and the 3x3 kernel as well

According to the source, yes, it is supposed to be for a typical Bayer CFA. The 3x3 kernel seems allright (cf. here section 1.2.2).

Edit: The kernel in (3.6) is for the green channel. The one for the red/blue channel can be computed by doing h_c^T h_c using h_c from (3.5).

But the green channel has this layout:

`gr` means green, `ng` means non green

``````ng gr ng gr ng
gr ng gr ng gr
ng gr ng gr ng
``````

Edit: Or was that for compressed channels, but then I don’t understand why there are zero weightings around the 3x3 centre block…

Edit: the red/blue channels have this layout (if not compressed):
Example for blue: `bl` means blue, `nb` means non blue:

``````bl nb bl nb bl
nb nb nb nb nb
bl nb bl nb bl
nb nb nb nb nb
bl nb bl nb bl
``````

@heckflosse Are you saying this is not the way to interpolate missing color values in a Bayered image?

``````x x x x x x x x
x B x B x B x B
x x x x * x x x
x B x B x B x B
``````

To find the B component of the marked (*) pixel, you can use the bilinear 3x3 kernel and look at the surrounding blue values and simply take the average. That is basically what equation (10) below tells me:

And that works similar for the other channels.
I kind of understand why the green channel must be different, because there are two G-pixels in a single Bayer-block. But I don’t understand at all how to derive the matrix. And for the more complicated case you can use a 7x7 bicubic kernel. The principle is the same, but again, I would like to understand how those matrices are derived.

No, I just got it wrong

Edit: I was confused by the channels being compressed (there is no green pixel with distance 1 to the centre green pixel in bayer array for example)