Efficient way to map a function over numpy matrix?

I'm trying to reproduce an algorithm over image, but failed to achieve the performance of PIL on Python.

For simplity, we take interpolation as an example.Supposed we have a matrix Im of luminance. For any point (x,y), we can compute the interpolation value by g(x,y)=f(floor(x),floor(y))+f(floor(x)+1,floor(y))+f(floor(x),floor(y)+1)+f(floor(x)+1,floor(y)+1) /4

Here is part of code. It takes tens of seconds to resize an image, that's inefficient. Also, it's not a element-wise mapping function. It involves with the whole matrix, or more precisely, the neighbour points of each point.

im = np.matrix(...) #A 512*512 image
axis = [x/(2047/511.) for x in xrange(2048)]
axis = [(x,y) for x in axis for y in axis] #resize to 2048*2048
im_temp = []
for (x, y) in axis:
    (l, k) = np.floor((x, y)).astype(int)
    a, b = x-l, y-k
    temp = (1-a)*(1-b)*im[l+1,k+1] + a*(1-b)*im[l+2,k+1] + (1-a)*b*im[l+1,k+2] + a*b*im[l+2,k+2]
    im_temp.append(temp)
np.asmatrix(im_temp).reshape((2048,2048)).astype(int)

How can we implement this algorithm in a more efficient way instead of 2 for loop?