Corresponding values for the Sun's ultra violet color maps?

Based on @ELNJ's answer I did a quick check of your posted image https://i.stack.imgur.com/Ky5Rl.png using the script below. Separating out the color channels we see the following:

channel   lowest value present

 red           4
 green        33
 blue         90
 alpha       255

So I think you can get dramatic results by subtracting an RGB value of [4, 33, 90]

which looks like this:

If that helps, or something along those lines does, please post an answer to your question, it is always okay to answer your own questions in Stack Exchange!

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Python script for plot:

import numpy as np
import matplotlib.pyplot as plt

img = plt.imread('Ky5Rl.png')
plt.imshow(img)
plt.show()

rgba = (255*np.moveaxis(img, 2, 0)).astype(int)
names = ['red', 'green', 'blue', 'alpha']
colors = names[:3] + ['black']
bins = np.arange(0, 257)
plt.figure()
plt.subplot(2, 1, 1)
plt.imshow(img)
plt.subplot(2, 1, 2)
for thing, name, color in zip(rgba, names, colors):
    a, b = np.histogram(thing.flatten(), bins=bins)  
    plt.plot(b[:-1], a, color=color)
    x = np.argmax(a)
    y = a[x]
    plt.annotate(name, (x, 1.01*y))
    print('first nonzero value for ', name, ' is at ', np.nonzero(a)[0][0])
plt.xlabel('value')
plt.ylabel('frequency')
plt.show()

It’s hard to tell for sure without seeing the data and playing around, but looking at your image, I would suggest trying to subtract off a constant background level to get the sky as close to black as possible. You could sample the edges of the image to find the average background level. Then subtract that value from the whole image array and try plotting it. Even simpler would be just to subtract the minimum value, but that’s prone to being skewed by just a single pixel.

If you try it, post an image back here and let’s see how it looks.