Multi-scale Gaussian Normalization#

This example applies Multi-scale Gaussian Normalization to a SunPy Map using sunkit_image.enhance.mgn.

import matplotlib.pyplot as plt
import sunpy.data.sample
import sunpy.map
from astropy import units as u
from matplotlib import colors

import sunkit_image.enhance as enhance

SunPy sample data contains a number of suitable images, which we will use here.

aia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)

# The original image is plotted to showcase the difference.
fig = plt.figure()
ax = plt.subplot(projection=aia_map)
aia_map.plot(clip_interval=(1, 99.99) * u.percent)
AIA $171 \; \mathrm{\mathring{A}}$ 2011-06-07 06:33:02
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<matplotlib.image.AxesImage object at 0x7f4f06522bf0>

Applying Multi-scale Gaussian Normalization on a solar image. The sunkit_image.enhance.mgn function takes a numpy.ndarray as a input so we will pass only the data part of GenericMap

out = enhance.mgn(aia_map.data)
# The value returned is also a numpy.ndarray so we convert it back to
# a  sunpy.map.GenericMap.
out = sunpy.map.Map(out, aia_map.meta)

Now we will plot the final result.

fig = plt.figure()
ax = plt.subplot(projection=out)
out.plot(norm=colors.Normalize())

plt.show()
AIA $171 \; \mathrm{\mathring{A}}$ 2011-06-07 06:33:02

Total running time of the script: (0 minutes 3.779 seconds)

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