直方圖全局均衡化

from skimage import exposure
# Equalization
img_eq = exposure.equalize_hist(img)

直方圖自適應均衡化

# Adaptive Equalization
# 參數(shù)2：Clipping limit, normalized between 0 and 1 (higher values give more contrast).
img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

直方圖對比度拉伸

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

實驗：直方圖全局均衡化、自適應均衡化霞玄、對比度拉伸效果對比

"""
======================
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. 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.

"""

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

from skimage import data, img_as_float
from skimage import exposure


matplotlib.rcParams['font.size'] = 8


def plot_img_and_hist(image, axes, bins=256):
    """Plot an image along with its histogram and cumulative histogram.

    """
    image = img_as_float(image)
    ax_img, ax_hist = axes
    # 共用x軸
    ax_cdf = ax_hist.twinx()

    # Display image
    ax_img.imshow(image, cmap=plt.cm.gray)
    ax_img.set_axis_off()

    # Display histogram
    ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')
    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
    ax_hist.set_xlabel('Pixel intensity')
    ax_hist.set_xlim(0, 1)
    ax_hist.set_yticks([])

    # Display cumulative distribution
    img_cdf, bins = exposure.cumulative_distribution(image, bins)
    ax_cdf.plot(bins, img_cdf, 'r')
    # 設置右側坐標軸為空
    ax_cdf.set_yticks([])

    return ax_img, ax_hist, ax_cdf


# Load an example image
img = data.moon()

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

# Equalization
img_eq = exposure.equalize_hist(img)

# Adaptive Equalization
# 參數(shù)2：Clipping limit, normalized between 0 and 1 (higher values give more contrast).
img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

# Display results
fig = plt.figure(figsize=(8, 5))
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)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()
ax_hist.set_ylabel('Number of pixels')
# 左側y軸范圍為0到y(tǒng)_max骤铃，5個刻度
ax_hist.set_yticks(np.linspace(0, y_max, 5))

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_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])
ax_img.set_title('Adaptive equalization')

ax_cdf.set_ylabel('Fraction of total intensity')
# 右側y軸范圍為0到1,5個刻度
ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels
fig.tight_layout()
plt.show()

對比度增強效果對比圖