Based on your description, you want scipy.ndimage.zoom.
Bilinear interpolation would be order=1, nearest is order=0, and cubic is the default (order=3).
zoom is specifically for regularly-gridded data that you want to resample to a new resolution.
As a quick example:
import numpy as np
import scipy.ndimage
x = np.arange(9).reshape(3,3)
print 'Original array:'
print x
print 'Resampled by a factor of 2 with nearest interpolation:'
print scipy.ndimage.zoom(x, 2, order=0)
print 'Resampled by a factor of 2 with bilinear interpolation:'
print scipy.ndimage.zoom(x, 2, order=1)
print 'Resampled by a factor of 2 with cubic interpolation:'
print scipy.ndimage.zoom(x, 2, order=3)
And the result:
Original array:
[[0 1 2]
[3 4 5]
[6 7 8]]
Resampled by a factor of 2 with nearest interpolation:
[[0 0 1 1 2 2]
[0 0 1 1 2 2]
[3 3 4 4 5 5]
[3 3 4 4 5 5]
[6 6 7 7 8 8]
[6 6 7 7 8 8]]
Resampled by a factor of 2 with bilinear interpolation:
[[0 0 1 1 2 2]
[1 2 2 2 3 3]
[2 3 3 4 4 4]
[4 4 4 5 5 6]
[5 5 6 6 6 7]
[6 6 7 7 8 8]]
Resampled by a factor of 2 with cubic interpolation:
[[0 0 1 1 2 2]
[1 1 1 2 2 3]
[2 2 3 3 4 4]
[4 4 5 5 6 6]
[5 6 6 7 7 7]
[6 6 7 7 8 8]]
Edit: As Matt S. pointed out, there are a couple of caveats for zooming multi-band images. I’m copying the portion below almost verbatim from one of my earlier answers:
Zooming also works for 3D (and nD) arrays. However, be aware that if you zoom by 2x, for example, you’ll zoom along all axes.
data = np.arange(27).reshape(3,3,3)
print 'Original:\n', data
print 'Zoomed by 2x gives an array of shape:', ndimage.zoom(data, 2).shape
This yields:
Original:
[[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]]
[[ 9 10 11]
[12 13 14]
[15 16 17]]
[[18 19 20]
[21 22 23]
[24 25 26]]]
Zoomed by 2x gives an array of shape: (6, 6, 6)
In the case of multi-band images, you usually don’t want to interpolate along the “z” axis, creating new bands.
If you have something like a 3-band, RGB image that you’d like to zoom, you can do this by specifying a sequence of tuples as the zoom factor:
print 'Zoomed by 2x along the last two axes:'
print ndimage.zoom(data, (1, 2, 2))
This yields:
Zoomed by 2x along the last two axes:
[[[ 0 0 1 1 2 2]
[ 1 1 1 2 2 3]
[ 2 2 3 3 4 4]
[ 4 4 5 5 6 6]
[ 5 6 6 7 7 7]
[ 6 6 7 7 8 8]]
[[ 9 9 10 10 11 11]
[10 10 10 11 11 12]
[11 11 12 12 13 13]
[13 13 14 14 15 15]
[14 15 15 16 16 16]
[15 15 16 16 17 17]]
[[18 18 19 19 20 20]
[19 19 19 20 20 21]
[20 20 21 21 22 22]
[22 22 23 23 24 24]
[23 24 24 25 25 25]
[24 24 25 25 26 26]]]