Added an example for advanced usage of WOW

examples/advanced_wow.py

@@ -0,0 +1,96 @@
+"""
+====================================================
+Advanced Usage of Wavelets Optimized Whitening (WOW)
+====================================================
+
+This example demonstrates different options of the Wavelets Optimized Whitening applied to a `sunpy.map.Map`
+using `sunkit_image.enhance.wow`.
+"""
+
+import matplotlib.pyplot as plt
+
+import sunpy.data.sample
+import sunpy.map
+
+import sunkit_image.enhance as enhance
+
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+from astropy.visualization import ImageNormalize, LinearStretch, PowerStretch, AsymmetricPercentileInterval
+
+###########################################################################
+# `sunpy` provides a range of sample data with a number of suitable images.
+# Here will just use AIA 193
+
+input_map = sunpy.map.Map(sunpy.data.sample.AIA_193_JUN2012)
+
+###########################################################################
+# We crop the southwestern quadrant
+
+top_right = SkyCoord(1200 * u.arcsec, 0 * u.arcsec, frame=input_map.coordinate_frame)
+bottom_left = SkyCoord(0 * u.arcsec, -1200 * u.arcsec, frame=input_map.coordinate_frame)
+input_map = input_map.submap(bottom_left, top_right=top_right)
+
+###########################################################################
+# We now applying different options of the Wavelets Optimized Whitening alogrithm.
+# The `sunkit_image.enhance.wow` function takes either a `sunpy.map.Map` or a `numpy.ndarray` as a input.
+# First, we call WOW with now arguments, which returns the default WOW enhancement.
+
+wow_map = enhance.wow(input_map)
+
+###########################################################################
+# The we denoise the output using soft thresholding in the three first wavelet scales with sigma = 5, 2, 1
+
+denoise_coefficients = [5, 2, 1]
+wow_map_denoised = enhance.wow(input_map,
+                               denoise_coefficients=denoise_coefficients)
+
+###########################################################################
+# We then run the edge-aware (bilateral) flavor of the algorithm. Its prevents ringing around sharp edges (e.g. the
+# solar limb, of very bright features.
+
+wow_map_bilateral = enhance.wow(input_map,
+                                bilateral=1)
+
+###########################################################################
+# This will call the edge-aware algorithm with denoising.
+
+wow_map_bilateral_denoised = enhance.wow(input_map,
+                                         bilateral=1,
+                                         denoise_coefficients=denoise_coefficients)
+
+###########################################################################
+# Finally, we merge the denoised edge-aware enhanced image with the gamma-stretched input, with weight h.
+
+gamma = 4
+wow_map_bilateral_denoised_merged = enhance.wow(input_map,
+                                                bilateral=1,
+                                                denoise_coefficients=denoise_coefficients,
+                                                gamma=gamma,
+                                                h=0.99)
+
+###########################################################################
+# Now we will plot the final result and compare that to the original image.
+
+fig = plt.figure(figsize=(8, 12))
+
+variations = {
+    'Input | gamma = {gamma} stretch': {'map': input_map, 'stretch': PowerStretch(1 / gamma)},
+    'WOW | linear stretch': {'map': wow_map, 'stretch': LinearStretch()},
+    'denoised WOW': {'map': wow_map_denoised, 'stretch': LinearStretch()},
+    'Edge-aware WOW': {'map': wow_map_bilateral, 'stretch': LinearStretch()},
+    'Edge-aware & denoised WOW': {'map': wow_map_bilateral_denoised, 'stretch': LinearStretch()},
+    'Merged with input': {'map': wow_map_bilateral_denoised_merged, 'stretch': LinearStretch()}
+}
+
+interval = AsymmetricPercentileInterval(1, 99.9)
+
+for i, (title, image) in enumerate(variations.items()):
+    ax = fig.add_subplot(3, 2, i + 1, projection=image['map'])
+    image['map'].plot(norm=ImageNormalize(image['map'].data, interval=interval, stretch=image['stretch']))
+    ax.set_title(title)
+    ax.axis('off')
+
+fig.tight_layout()
+
+plt.show()