Python 图像增强算法实现详解

使用python编写了共六种图像增强算法:

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)限制对比度自适应直方图均衡化:CLAHE6)retinex-SSR7)retinex-MSR其中,6和7属于同一种下的变化。将每种方法编写成一个函数,封装,可以直接在主函数中调用。采用同一幅图进行效果对比。

图像增强的效果为:

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CLAHE和retinex的效果均较好

python代码为:

# 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)
  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("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()

增强效果如下图所示:

D3530773-EC96-A708-81DD-AF5F4DAB3BB9.png

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