本文用 Python 实现 PS 图像调整中的亮度调整，具体的算法原理和效果可以参考之前的博客：

http://blog.csdn.net/matrix_space/article/details/22991683

import matplotlib.pyplot as plt

from skimage import io

file_name='D:/Image Processing/PS Algorithm/4.jpg';

img=io.imread(file_name)

Increment = -10.0

img = img * 1.0

I = (img[:, :, 0] + img[:, :, 1] + img[:, :, 2])/3.0 + 0.001

mask_1 = I > 128.0

r = img [:, :, 0]

g = img [:, :, 1]

b = img [:, :, 2]

rhs = (r*128.0 - (I - 128.0) * 256.0) / (256.0 - I)

ghs = (g*128.0 - (I - 128.0) * 256.0) / (256.0 - I)

bhs = (b*128.0 - (I - 128.0) * 256.0) / (256.0 - I)

rhs = rhs * mask_1 + (r * 128.0 / I) * (1 - mask_1)

ghs = ghs * mask_1 + (g * 128.0 / I) * (1 - mask_1)

bhs = bhs * mask_1 + (b * 128.0 / I) * (1 - mask_1)

I_new = I + Increment - 128.0

mask_2 = I_new > 0.0

R_new =  rhs + (256.0-rhs) * I_new / 128.0

G_new =  ghs + (256.0-ghs) * I_new / 128.0

B_new =  bhs + (256.0-bhs) * I_new / 128.0

R_new = R_new * mask_2 + (rhs + rhs * I_new/128.0) * (1-mask_2)

G_new = G_new * mask_2 + (ghs + ghs * I_new/128.0) * (1-mask_2)

B_new = B_new * mask_2 + (bhs + bhs * I_new/128.0) * (1-mask_2)

Img_out = img * 1.0

Img_out[:, :, 0] = R_new

Img_out[:, :, 1] = G_new

Img_out[:, :, 2] = B_new

Img_out = Img_out/255.0

# 饱和处理

mask_1 = Img_out  < 0

mask_2 = Img_out  > 1

Img_out = Img_out * (1-mask_1)

Img_out = Img_out * (1-mask_2) + mask_2

plt.figure()

plt.imshow(img/255.0)

plt.axis('off')

plt.figure(2)

plt.imshow(Img_out)

plt.axis('off')

plt.figure(3)

plt.imshow(I/255.0, plt.cm.gray)

plt.axis('off')

plt.show()