Gamma校正

Gamma校正是对输入图像灰度值进行的非线性操作，使输出图像灰度值与输入图像灰度值呈指数关系：

这个指数即为Gamma。

Gamma校正的原理很简单，就一个很简单的表达式，如下图所示：

其中V_in的取值范围是0~1，最重要的参数就是公式中的γ参数！

γ的值决定了输入图像和输出图像之间的灰度映射方式，即决定了是增强低灰度值区域还是增高灰度值区域。

γ>1时，图像的高灰度区域对比度得到增强。

γ<1时，图像的低灰度区域对比度得到增强。

γ=1时，不改变原图像。

伽马变换对于图像对比度偏低，并且整体亮度值偏高(对于于相机过曝)情况下的图像增强效果明显。

对数log变换

log 函数的表达式:

y=alog(1+x), a 是一个放大系数，x 同样是输入的像素值，取值范围为 [0−1], y 是输出的像素值。

对数变换对于整体对比度偏低并且灰度值偏低的图像增强效果较好。

skimage库实现gamam校正和log校正

函数：

Gamma：

gamma_corrected = exposure.adjust_gamma(img, 2)

Logarithmic：

logarithmic_corrected = exposure.adjust_log(img, 1)

"""

=================================

Gamma and log contrast adjustment

=================================

This example adjusts image contrast by performing a Gamma and a Logarithmic

correction on the input image.

"""

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

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')

ax_cdf.set_yticks([])

return ax_img, ax_hist, ax_cdf

# Load an example image

img = data.moon()

# Gamma

gamma_corrected = exposure.adjust_gamma(img, 2)

# Logarithmic

logarithmic_corrected = exposure.adjust_log(img, 1)

# Display results

fig = plt.figure(figsize=(8, 5))

axes = np.zeros((2, 3), dtype=np.object)

axes[0, 0] = plt.subplot(2, 3, 1)

axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0])

axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0])

axes[1, 0] = plt.subplot(2, 3, 4)

axes[1, 1] = plt.subplot(2, 3, 5)

axes[1, 2] = plt.subplot(2, 3, 6)

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')

ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected, axes[:, 1])

ax_img.set_title('Gamma correction')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected, axes[:, 2])

ax_img.set_title('Logarithmic correction')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

实验结果