Visualizing noisy images

You have an image. Each pixel has a value with some uncertainty. How do you visualize the uncertainty in each pixel? Like this:

Here's the Python code

import numpy as np
from matplotlib import pyplot as plt

class FlickerImage(object):
    def __init__(self, im, err):
        self.im = im.copy()
        self.err = err.copy()
        finite = np.isfinite(self.im + self.err)
        self.vmin = (self.im - 2 * self.err)[finite].min()
        self.vmax = (self.im + 2 * self.err)[finite].max()
        self.im[np.invert(finite)] = self.vmax
        self.err[np.invert(finite)] = 0
    def flicker(self):
        fg = plt.imshow(np.zeros(self.im.shape),
                        interpolation='nearest',
                        vmin=self.vmin,
                        vmax=self.vmax)
        while True:
            ran = np.random.normal(size=im.shape)
            fg.set_data(im + err * ran)
            plt.draw()

And here's an example script:

import pyfits
f = pyfits.open('file.fits')
im = f["IMAGE"].data
err = f["ERROR"].data
flicker_image = FlickerImage(im, err)
flicker_image.flicker()