自学教程：python 图像增强算法实现详解

# 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 cv2import numpy as npimport 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CLAHEdef 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_clahedef replaceZeroes(data):  min_nonzero = min(data[np.nonzero(data)])  data[data == 0] = min_nonzero  return data# retinex SSRdef 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_uint8def 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 MMRdef 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_uint8def 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 resultif __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()