Histogram equalization uses a monotonic and a non-linear mapping which reassigns the pixel intensity values in the input image in such a way that the output image has a uniform distribution of intensities (a flat histogram), and thereby enhances the contrast of the image. The following screenshot describes the transformation function for histogram equalization:

The following code block shows how to use the exposure module's equalize_hist() function to do histogram equalization with scikit-image. The histogram equalization implementation has two different flavors: one is a global operation over the entire image, while the second is local (adaptive) and done by dividing the image into blocks and running histogram equalization on each of them:

img = rgb2gray(imread('../images/earthfromsky.jpg'))
# histogram equalization
img_eq = exposure.equalize_hist(img)
# adaptive histogram equalization
img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
pylab.gray()
images = [img, img_eq, img_adapteq]
titles = ['original input (earth from sky)', 'after histogram equalization', 'after adaptive histogram equalization']
for i in range(3):
    pylab.figure(figsize=(20,10)), plot_image(images[i], titles[i])
pylab.figure(figsize=(15,5))
for i in range(3):
    pylab.subplot(1,3,i+1), pylab.hist(images[i].ravel(), color='g'), pylab.title(titles[i], size=15)
pylab.show()

The following screenshots show the output of the previous code block. As can be seen, after histogram equalization, the output image histogram becomes almost uniform (the x axis represents pixel values and the y axis corresponding frequencies), although adaptive histogram equalization reveals the details of the image more clearly than the global histogram equalization:

The following screenshot shows how the distribution of pixels change for local(near-uniform)versus adaptive (stretched and piece-wise uniform) histogram equalization:

The following code block compares the image enhancements obtained using two different histogram processing techniques, namely contrast stretching and histogram equalization, with scikit-image:

import matplotlib
matplotlib.rcParams['font.size'] = 8
def plot_image_and_hist(image, axes, bins=256):
    image = img_as_float(image)
    axes_image, axes_hist = axes
    axes_cdf = axes_hist.twinx()
    axes_image.imshow(image, cmap=pylab.cm.gray)
    axes_image.set_axis_off()
    axes_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')
    axes_hist.set_xlim(0, 1)
    axes_hist.set_xlabel('Pixel intensity', size=15)
    axes_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
    axes_hist.set_yticks([])
    image_cdf, bins = exposure.cumulative_distribution(image, bins)
    axes_cdf.plot(bins, image_cdf, 'r')
    axes_cdf.set_yticks([])
    return axes_image, axes_hist, axes_cdf

im = io.imread('../images/beans_g.png')
# contrast stretching
im_rescale = exposure.rescale_intensity(im, in_range=(0, 100), out_range=(0, 255))
im_eq = exposure.equalize_hist(im) # histogram equalization
im_adapteq = exposure.equalize_adapthist(im, clip_limit=0.03) # adaptive histogram equalization

fig = pylab.figure(figsize=(15, 7))
axes = np.zeros((2, 4), dtype = np.object)
axes[0, 0] = fig.add_subplot(2, 4, 1)
for i in range(1, 4):
    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
for i in range(0, 4):
    axes[1, i] = fig.add_subplot(2, 4, 5+i)
axes_image, axes_hist, axes_cdf = plot_image_and_hist(im, axes[:, 0])
axes_image.set_title('Low contrast image', size=20)
y_min, y_max = axes_hist.get_ylim()
axes_hist.set_ylabel('Number of pixels', size=20)
axes_hist.set_yticks(np.linspace(0, y_max, 5))
axes_image, axes_hist, axes_cdf = plot_image_and_hist(im_rescale, axes[:,1])
axes_image.set_title('Contrast stretching', size=20)
axes_image, axes_hist, axes_cdf = plot_image_and_hist(im_eq, axes[:, 2])
axes_image.set_title('Histogram equalization', size=20)
axes_image, axes_hist, axes_cdf = plot_image_and_hist(im_adapteq, axes[:,3])
axes_image.set_title('Adaptive equalization', size=20)
axes_cdf.set_ylabel('Fraction of total intensity', size=20)
axes_cdf.set_yticks(np.linspace(0, 1, 5))
fig.tight_layout()
pylab.show()

The following screenshot shows the output of the previous code. As can be seen, adaptive histogram equalization provides better results than histogram equalization in terms of making the details of the output image clearer:

Using the low-contrast colored cheetah input image instead, the previous code produces the following output: