# 圖像增強算法，圖像銳化算法
# 1）基于直方圖均衡化 2）基于拉普拉斯算子 3）基于對數變換 4）基于伽馬變換 5)CLAHE 6)retinex-SSR 7)retinex-MSR
# 其中，基于拉普拉斯算子的圖像增強為利用空域卷積運算實現濾波
# 基于同一圖像對比增強效果
# 直方圖均衡化:對比度較低的圖像適合使用直方圖均衡化方法來增強圖像細節
# 拉普拉斯算子可以增強局部的圖像對比度
# log對數變換對于整體對比度偏低并且灰度值偏低的圖像增強效果較好
# 伽馬變換對于圖像對比度偏低，并且整體亮度值偏高（對于相機過曝）情況下的圖像增強效果明顯
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 直方圖均衡增強
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)
return image_lap
# 對數變換
def log(image):
image_log = np.uint8(np.log(np.array(image) + 1))
cv2.normalize(image_log, image_log, 0, 255, cv2.NORM_MINMAX)
# 轉換成8bit圖像顯示
cv2.convertScaleAbs(image_log, image_log)
return image_log
# 伽馬變換
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
# 限制對比度自適應直方圖均衡化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("example.jpg")
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plt.subplot(4, 2, 1)
plt.imshow(image)
plt.axis('off')
plt.title('Offical')
# 直方圖均衡增強
image_equal_clo = hist(image)
plt.subplot(4, 2, 2)
plt.imshow(image_equal_clo)
plt.axis('off')
plt.title('equal_enhance')
# 拉普拉斯算法增強
image_lap = laplacian(image)
plt.subplot(4, 2, 3)
plt.imshow(image_lap)
plt.axis('off')
plt.title('laplacian_enhance')
# LoG對象算法增強
image_log = log(image)
plt.subplot(4, 2, 4)
plt.imshow(image_log)
plt.axis('off')
plt.title('log_enhance')
# 伽馬變換
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()
到此這篇關于python 圖像增強算法實現詳解的文章就介紹到這了,更多相關python 圖像增強算法內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
