本文实例为大家分享了python实现图像增强算法的具体代码，供大家参考，具体内容如下
图像增强算法，图像锐化算法
1）基于直方图均衡化
2）基于拉普拉斯算子
3）基于对数变换
4）基于伽马变换
5) CLAHE
6)retinex-SSR
7)retinex-MSR
其中，基于拉普拉斯算子的图像增强为利用空域卷积运算实现滤波
基于同一图像对比增强效果
直方图均衡化:对比度较低的图像适合使用直方图均衡化方法来增强图像细节
拉普拉斯算子可以增强局部的图像对比度
log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
伽马变换对于图像对比度偏低，并且整体亮度值偏高（对于相机过曝）情况下的图像增强效果明显
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 直方图均衡增强
def hist(image):
r, g, b = cv2.split(image)
r1 = cv2.equalizeHist(r)
g1 = cv2.equalizeHist(g)
b1 = cv2.equalizeHist(b)
image_equal_clo = cv2.merge([r1, g1, b1])
return image_equal_clo
# 拉普拉斯算子
def laplacian(image):
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
image_lap = cv2.filter2D(image, cv2.CV_8UC3, kernel)
# cv2.imwrite('th1.webp', image_lap)
return image_lap
# 对数变换
def log(image):
image_log = np.uint8(np.log(np.array(image) + 1))
cv2.normalize(image_log, image_log, 0, 255, cv2.NORM_MINMAX)
# 转换成8bit图像显示
cv2.convertScaleAbs(image_log, image_log)
return image_log
# 伽马变换
def gamma(image):
fgamma = 2
image_gamma = np.uint8(np.power((np.array(image) / 255.0), fgamma) * 255.0)
cv2.normalize(image_gamma, image_gamma, 0, 255, cv2.NORM_MINMAX)
cv2.convertScaleAbs(image_gamma, image_gamma)
return image_gamma
# 限制对比度自适应直方图均衡化CLAHE
def clahe(image):
b, g, r = cv2.split(image)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
b = clahe.apply(b)
g = clahe.apply(g)
r = clahe.apply(r)
image_clahe = cv2.merge([b, g, r])
return image_clahe
def replaceZeroes(data):
min_nonzero = min(data[np.nonzero(data)])
data[data == 0] = min_nonzero
return data
# retinex SSR
def SSR(src_img, size):
L_blur = cv2.GaussianBlur(src_img, (size, size), 0)
img = replaceZeroes(src_img)
L_blur = replaceZeroes(L_blur)
dst_Img = cv2.log(img/255.0)
dst_Lblur = cv2.log(L_blur/255.0)
dst_IxL = cv2.multiply(dst_Img, dst_Lblur)
log_R = cv2.subtract(dst_Img, dst_IxL)
dst_R = cv2.normalize(log_R,None, 0, 255, cv2.NORM_MINMAX)
log_uint8 = cv2.convertScaleAbs(dst_R)
return log_uint8
def SSR_image(image):
size = 3
b_gray, g_gray, r_gray = cv2.split(image)
b_gray = SSR(b_gray, size)
g_gray = SSR(g_gray, size)
r_gray = SSR(r_gray, size)
result = cv2.merge([b_gray, g_gray, r_gray])
return result
# retinex MMR
def MSR(img, scales):
weight = 1 / 3.0
scales_size = len(scales)
h, w = img.shape[:2]
log_R = np.zeros((h, w), dtype=np.float32)
for i in range(scales_size):
img = replaceZeroes(img)
L_blur = cv2.GaussianBlur(img, (scales[i], scales[i]), 0)
L_blur = replaceZeroes(L_blur)
dst_Img = cv2.log(img/255.0)
dst_Lblur = cv2.log(L_blur/255.0)
dst_Ixl = cv2.multiply(dst_Img, dst_Lblur)
log_R += weight * cv2.subtract(dst_Img, dst_Ixl)
dst_R = cv2.normalize(log_R,None, 0, 255, cv2.NORM_MINMAX)
log_uint8 = cv2.convertScaleAbs(dst_R)
return log_uint8
def MSR_image(image):
scales = [15, 101, 301] # [3,5,9]
b_gray, g_gray, r_gray = cv2.split(image)
b_gray = MSR(b_gray, scales)
g_gray = MSR(g_gray, scales)
r_gray = MSR(r_gray, scales)
result = cv2.merge([b_gray, g_gray, r_gray])
return result
if __name__ == "__main__":
image = cv2.imread('img/FJ(93).webp')
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plt.subplot(4, 2, 1)
plt.imshow(image)
plt.axis('off')
plt.title('Offical')
# 直方图均衡增强
image_equal_clo = hist(image)
plt.subplot(4, 2, 2)
plt.imshow(image_equal_clo)
plt.axis('off')
plt.title('equal_enhance')
# 拉普拉斯算法增强
image_lap = laplacian(image)
plt.subplot(4, 2, 3)
plt.imshow(image_lap)
plt.axis('off')
plt.title('laplacian_enhance')
# LoG对象算法增强
image_log = log(image)
plt.subplot(4, 2, 4)
plt.imshow(image_log)
plt.axis('off')
plt.title('log_enhance')
# # 伽马变换
image_gamma = gamma(image)
plt.subplot(4, 2, 5)
plt.imshow(image_gamma)
plt.axis('off')
plt.title('gamma_enhance')
# CLAHE
image_clahe = clahe(image)
plt.subplot(4, 2, 6)
plt.imshow(image_clahe)
plt.axis('off')
plt.title('CLAHE')
# retinex_ssr
image_ssr = SSR_image(image)
plt.subplot(4, 2, 7)
plt.imshow(image_ssr)
plt.axis('off')
plt.title('SSR')
# retinex_msr
image_msr = MSR_image(image)
plt.subplot(4, 2, 8)
plt.imshow(image_msr)
plt.axis('off')
plt.title('MSR')
plt.show()
