• Can you provide a minimal example so that we can test?
• Are you using a high-resolution screen with display scaling > 1?
• Even though you say you are "not interested in "nearest" etc.", can you try interpolation="linear"? In particular for downsampling it should not bee too different from bicubic.

Are you resampling before or after color mapping?

The relevant code is

This discussion in #13724 may also be of interest.

Without a minimal example to test with or any context as to how you generated your second image there isn't much we can do to help you.

Thank you so far. I checked and its not a resultion / windows magnification issue. The other hint of how the image is resampled gets closer. However, I'm a user and don't know where to find the "_resample" code.
Nevertheless, I made a minimum example. The array is much larger than the pixel resolution of the figure and I'm plotting this array with interpolation of 'none','nearest' and 'bicubic'.
The 'bicubic' looks nice .. but I still don't understand, when the bicubic DOWN-sampling does (that much) blurring.
For comparision, I made a last plot where I manually downsampled. I expected something like this to see in the high res 'bicubic' version as well. You can change the downsampling factor to e.g. 1/4 or the order to 2, but as it then does not need any interpolation, no blurring occurs.

Code for reproduction

import matplotlib.pyplot as plt
import numpy as np
import math
from scipy import ndimage
Nx=2048
Ny=2048
gw=4

x = np.arange(Nx)
y = np.arange(Ny)

X, Y = np.meshgrid(x, y)
a = np.exp(-(X-Nx//2+(-Y+Ny//2)*4)**2/gw**2)

fig = plt.figure()

i=1
for intp in ['none', 'nearest', 'bicubic']:
    ax = fig.add_subplot(2,2,i)
    ax.imshow(a,origin='lower', aspect='auto',cmap='jet',interpolation=intp)
    i+=1

b = ndimage.zoom(a,1.0/math.pi**2,order=0)
ax = fig.add_subplot(2, 2, i)
ax.imshow(b, origin='lower', aspect='auto', cmap='jet', interpolation='none')

plt.show()

https://matplotlib.org/devdocs/gallery/images_contours_and_fields/image_antialiasing.html Basically if you sub-sample you should smooth first so you don’t get aliasing of small scales into large scales.

Ok, I hope I understand: When downsampling, the original is smoothed first (antialiased?), then downsampled. It means, that any of the interpolations are then actually not used, but indicate, that the smoothing should be done? Only for 'none' and 'nearest' there is no smoothing before the downsampling. Thats why, I was confused. I think I have seens an intermediate parameter named "antialiased" in one of the posted discussions, but it did not make it to a release ...

If you downsample the “interpolation” operation is still applied to get the downsampled points. This acts as a smoothing unless you choose nearest. But nearest is a terrible way to down sample a signal because it causes moire patterns.

The “antialiased” method applies the smoothing (Hanning) if downsampling or resembling within a factor of two and no interpolation if not. I’m not at my computer so can’t check what version that will be released in. Maybe 3.3

interpolation='antialiased' is in version 3.2, which is released.

How are you diagnosing "blur"? If I set the dpi of the figure to 100 or so and save as a png, then I get speckles for the subsampled data, and a straight line about one or two pixels wide for the smoothed version. (I've zoomed and screenshotted to see the detail). Thats basically how anti-aliasing works: you smooth at slightly larger scale than your new Nyquist wavelength and then sub-sample. The cost is that the resulting signal is a bit smoothed out.

None:

Bicubic:

I think this clarifies it, thank you very much. Saving it to a png helped, it might be that the original screenshot was an overlay of bicubic downsampling and the windows screen zoom settings.

Right it’s confusing to try and figure these things out when the number of pixels In the image changes when you zoom.