图像加噪及平滑处理

  1 random_noise(image, mode=‘gaussian‘, seed=None, clip=True, **kwargs)
  2     Function to add random noise of various types to a floating-point image.
  3 
  4     Parameters
  5     ----------
  6     image : ndarray
  7         Input image data. Will be converted to float.
  8     mode : str, optional
  9         One of the following strings, selecting the type of noise to add:
 10 
 11         - ‘gaussian‘  Gaussian-distributed additive noise.
 12         - ‘localvar‘  Gaussian-distributed additive noise, with specified
 13                       local variance at each point of `image`.
 14         - ‘poisson‘   Poisson-distributed noise generated from the data.
 15         - ‘salt‘      Replaces random pixels with 1.
 16         - ‘pepper‘    Replaces random pixels with 0 (for unsigned images) or
 17                       -1 (for signed images).
 18         - ‘s&p‘       Replaces random pixels with either 1 or `low_val`, where
 19                       `low_val` is 0 for unsigned images or -1 for signed
 20                       images.
 21         - ‘speckle‘   Multiplicative noise using out = image + n*image, where
 22                       n is uniform noise with specified mean & variance.
 23     seed : int, optional
 24         If provided, this will set the random seed before generating noise,
 25         for valid pseudo-random comparisons.
 26     clip : bool, optional
 27         If True (default), the output will be clipped after noise applied
 28         for modes `‘speckle‘`, `‘poisson‘`, and `‘gaussian‘`. This is
 29         needed to maintain the proper image data range. If False, clipping
 30         is not applied, and the output may extend beyond the range [-1, 1].
 31     mean : float, optional
 32         Mean of random distribution. Used in ‘gaussian‘ and ‘speckle‘.
 33         Default : 0.
 34     var : float, optional
 35         Variance of random distribution. Used in ‘gaussian‘ and ‘speckle‘.
 36         Note: variance = (standard deviation) ** 2. Default : 0.01
 37     local_vars : ndarray, optional
 38         Array of positive floats, same shape as `image`, defining the local
 39         variance at every image point. Used in ‘localvar‘.
 40     amount : float, optional
 41         Proportion of image pixels to replace with noise on range [0, 1].
 42         Used in ‘salt‘, ‘pepper‘, and ‘salt & pepper‘. Default : 0.05
 43     salt_vs_pepper : float, optional
 44         Proportion of salt vs. pepper noise for ‘s&p‘ on range [0, 1].
 45         Higher values represent more salt. Default : 0.5 (equal amounts)
 46 
 47     Returns
 48     -------
 49     out : ndarray
 50         Output floating-point image data on range [0, 1] or [-1, 1] if the
 51         input `image` was unsigned or signed, respectively.
 52 
 53     Notes
 54     -----
 55     Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside
 56     the valid image range. The default is to clip (not alias) these values,
 57     but they may be preserved by setting `clip=False`. Note that in this case
 58     the output may contain values outside the ranges [0, 1] or [-1, 1].
 59     Use this option with care.
 60 
 61     Because of the prevalence of exclusively positive floating-point images in
 62     intermediate calculations, it is not possible to intuit if an input is
 63     signed based on dtype alone. Instead, negative values are explicitly
 64     searched for. Only if found does this function assume signed input.
 65     Unexpected results only occur in rare, poorly exposes cases (e.g. if all
 66     values are above 50 percent gray in a signed `image`). In this event,
 67     manually scaling the input to the positive domain will solve the problem.
 68 
 69     The Poisson distribution is only defined for positive integers. To apply
 70     this noise type, the number of unique values in the image is found and
 71     the next round power of two is used to scale up the floating-point result,
 72     after which it is scaled back down to the floating-point image range.
 73 
 74     To generate Poisson noise against a signed image, the signed image is
 75     temporarily converted to an unsigned image in the floating point domain,
 76     Poisson noise is generated, then it is returned to the original range.
 77 

  1 import skimage
  2 import numpy as np
  3 import cv2 as cv
  4 
  5 img = cv.imread(r‘Lena.png‘)
  6 imgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
  7 imgNoise = skimage.util.random_noise(imgGray,mode=‘s&p‘)
  8 cv.imshow(‘LenaGray‘,imgGray)
  9 cv.imshow(‘LenaNoise‘,imgNoise)
 10 
 11 cv.waitKey(0)
 12 cv.destroyAllWindows()

  1 import matplotlib.pyplot as plt
  2 import numpy as np
  3 import cv2 as cv
  4 
  5 plt.rcParams[‘font.sans-serif‘] = [‘SimHei‘]  # 用来正常显示中文标签
  6 
  7 img = cv.imread(r‘LenaNoise.png‘)
  8 # 均值滤波
  9 averFil = cv.blur(img, (5,5))
 10 averFil30 = cv.blur(img,(30,30))
 11 # 方框滤波
 12 boxFil = cv.boxFilter(img, -1, (2,2), normalize=0)
 13 boxFil5 = cv.boxFilter(img, -1, (5,5), normalize=0)
 14 # 高斯滤波
 15 GaussFil = cv.GaussianBlur(img, (5,5), 0, 0)
 16 # 中值滤波
 17 medianFil = cv.medianBlur(img, 3)
 18 # 双边滤波
 19 bilFil = cv.bilateralFilter(img, 9, 255, 255)
 20 # 2D卷积
 21 kernel = np.ones((9,9),np.float32)/81
 22 fil2D = cv.filter2D(img, -1, kernel)
 23 
 24 titles = [‘original‘,‘均值滤波5*5‘,‘均值滤波30*30‘,‘方框滤波2*2‘,‘方框滤波5*5‘,‘高斯滤波‘,‘中值滤波‘,‘双边滤波‘,‘2D卷积‘]
 25 images = [img,averFil,averFil30,boxFil,boxFil5,GaussFil,medianFil,bilFil,fil2D]
 26 for i in range(9):
 27     plt.subplot(3,3,i+1), plt.imshow(images[i],‘gray‘)
 28     plt.title(titles[i])
 29     plt.xticks([]), plt.yticks([])
 30 plt.show()