Wavelets Optimized Whitening (WOW)

This example applies Wavelets Optimized Whitening to a sunpy.map.Map using sunkit_image.enhance.wow.

import matplotlib.pyplot as plt

from astropy import units as u

import sunpy.data.sample
import sunpy.map

import sunkit_image.enhance as enhance

sunpy provides a range of sample data with a number of suitable images. Here will just use AIA 171

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

/home/docs/checkouts/readthedocs.org/user_builds/sunkit-image/conda/latest/lib/python3.12/site-packages/astropy/io/fits/hdu/image.py:631: VerifyWarning: Invalid 'BLANK' keyword in header.  The 'BLANK' keyword is only applicable to integer data, and will be ignored in this HDU.
  warnings.warn(msg, VerifyWarning)

Applying Wavelets Optimized Whitening on a solar image.

The sunkit_image.enhance.wow function takes either a sunpy.map.Map or a numpy.ndarray as a input. We will use the bilateral flavor of the algorithm, and denoising coefficients in the first three wavelet planes equal to 5, 2, & 1 sigma of the local noise. The noise is estimated automatically. It is possible to pass a noise map for more optimal results.

wow_map = enhance.wow(aia_map, bilateral=1, denoise_coefficients=[5, 2, 1])

Now we will plot the final result and compare that to the original image.

fig = plt.figure(figsize=(10, 7))

ax = fig.add_subplot(121, projection=aia_map)
aia_map.plot(axes=ax, clip_interval=(1, 99.99) * u.percent)

ax1 = fig.add_subplot(122, projection=wow_map)
wow_map.plot(axes=ax1, clip_interval=(1, 99.99) * u.percent, norm=None)
ax1.set_title("Wavelets Optimized Whitening (WOW)")

ax1.coords[1].set_ticks_visible(False)
ax1.coords[1].set_ticklabel_visible(False)
fig.tight_layout()

plt.show()