Histogram Equalization

This examples enhances an image with low contrast, using a method called histogram equalization, which “spreads out the most frequent intensity values” in an image [1]. The equalized image has a roughly linear cumulative distribution function.

While histogram equalization has the advantage that it requires no parameters, it sometimes yields unnatural looking images. An alternative method is contrast stretching, where the image is rescaled to include all intensities that fall within the 2nd and 98th percentiles [2].

[1]	http://en.wikipedia.org/wiki/Histogram_equalization

[2]	http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm

from skimage import data
from skimage.util.dtype import dtype_range
from skimage import exposure

import matplotlib.pyplot as plt

import numpy as np

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 = data.moon()

# Contrast stretching
p2 = np.percentile(img, 2)
p98 = np.percentile(img, 98)
img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))

# Equalization
img_eq = exposure.equalize(img)


# Display results
f, axes = plt.subplots(2, 3, figsize=(8, 4))

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('Contrast stretching')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
ax_img.set_title('Histogram equalization')
ax_cdf.set_ylabel('Fraction of total intensity')


# prevent overlap of y-axis labels
plt.subplots_adjust(wspace=0.4)
plt.show()

Python source code: download (generated using mpltools 0.6dev)