This examples enhances an image with low contrast, using a method called local histogram equalization, which spreads out the most frequent intensity values in an image.
The equalized image [1] has a roughly linear cumulative distribution function for each pixel neighborhood.
The local version [2] of the histogram equalization emphasized every local graylevel variations.
| [1] | http://en.wikipedia.org/wiki/Histogram_equalization |
| [2] | http://en.wikipedia.org/wiki/Adaptive_histogram_equalization |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from skimage.util.dtype import dtype_range
from skimage.util import img_as_ubyte
from skimage import exposure
from skimage.morphology import disk
from skimage.filter import rank
matplotlib.rcParams['font.size'] = 9
def plot_img_and_hist(img, axes, bins=256):
"""Plot an image along with its histogram and cumulative histogram.
"""
ax_img, ax_hist = axes
ax_cdf = ax_hist.twinx()
# Display image
ax_img.imshow(img, cmap=plt.cm.gray)
ax_img.set_axis_off()
# Display histogram
ax_hist.hist(img.ravel(), bins=bins)
ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
ax_hist.set_xlabel('Pixel intensity')
xmin, xmax = dtype_range[img.dtype.type]
ax_hist.set_xlim(xmin, xmax)
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(img, bins)
ax_cdf.plot(bins, img_cdf, 'r')
return ax_img, ax_hist, ax_cdf
# Load an example image
img = img_as_ubyte(data.moon())
# Global equalize
img_rescale = exposure.equalize_hist(img)
# Equalization
selem = disk(30)
img_eq = rank.equalize(img, selem=selem)
# Display results
fig, axes = plt.subplots(2, 3, figsize=(8, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Low contrast image')
ax_hist.set_ylabel('Number of pixels')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
ax_img.set_title('Global equalise')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
ax_img.set_title('Local equalize')
ax_cdf.set_ylabel('Fraction of total intensity')
# prevent overlap of y-axis labels
fig.subplots_adjust(wspace=0.4)
plt.show()
STDOUT
STDERR
Python source code: download (generated using skimage 0.11dev)
IPython Notebook: download (generated using skimage 0.11dev)