Question

i have a 128×128 array of elevation data (elevations from -400m to 8000m are displayed using 9 colors) and i need to resize it to 512×512. I did it with bicubic interpolation, but the result looks weird. In the picture you can see original, nearest and bicubic. Note: only the elevation data are interpolated not the colors themselves (gamut is preserved). Are those artifacts seen on the bicubic image result of my bad interpolation code or they are caused by the interpolating of discrete (9 steps) data?

https://i.stack.imgur.com/Qx2cl.png

Answer 1

There must be something wrong with the bicubic code you’re using. Here’s my result with Python:

The black border around the outside is where the result was outside of the palette due to ringing.

Here’s the program that produced the above:

from PIL import Image
im = Image.open(r'c:\temp\temp.png')

# convert the image to a grayscale with 8 values from 10 to 17
levels=((0,0,255),(1,255,0),(255,255,0),(255,0,0),(255,175,175),(255,0,255),(1,255,255),(255,255,255))
img = Image.new('L', im.size)
iml = im.load()
imgl = img.load()
colormap = {}
for i, color in enumerate(levels):
    colormap[color] = 10 + i
width, height = im.size
for y in range(height):
    for x in range(width):
        imgl[x,y] = colormap[iml[x,y]]

# resize using Bicubic and restore the original palette
im4x = img.resize((4*width, 4*height), Image.BICUBIC)
palette = []
for i in range(256):
    if 10 <= i < 10+len(levels):
        palette.extend(levels[i-10])
    else:
        palette.extend((i, i, i))
im4x.putpalette(palette)
im4x.save(r'c:\temp\temp3.png')

Edit: Evidently Python’s Bicubic isn’t the best either. Here’s what I was able to do by hand in Paint Shop Pro, using roughly the same procedure as above.