实现对图像进行简单的高斯去噪和椒盐去噪。

代码如下：

import numpy as npfrom PIL import Imageimport matplotlib.pyplot as pltimport randomimport scipy.miscimport scipy.signalimport scipy.ndimagefrom matplotlib.font_manager import FontPropertiesfont_set = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=10) def medium_filter(im, x, y, step): sum_s = [] for k in range(-int(step / 2), int(step / 2) + 1): for m in range(-int(step / 2), int(step / 2) + 1): sum_s.append(im[x + k][y + m]) sum_s.sort() return sum_s[(int(step * step / 2) + 1)] def mean_filter(im, x, y, step): sum_s = 0 for k in range(-int(step / 2), int(step / 2) + 1): for m in range(-int(step / 2), int(step / 2) + 1): sum_s += im[x + k][y + m] / (step * step) return sum_s def convert_2d(r): n = 3 # 3*3 滤波器, 每个系数都是 1/9 window = np.ones((n, n)) / n ** 2 # 使用滤波器卷积图像 # mode = same 表示输出尺寸等于输入尺寸 # boundary 表示采用对称边界条件处理图像边缘 s = scipy.signal.convolve2d(r, window, mode='same', boundary='symm') return s.astype(np.uint8) def convert_3d(r): s_dsplit = [] for d in range(r.shape[2]): rr = r[:, :, d] ss = convert_2d(rr) s_dsplit.append(ss) s = np.dstack(s_dsplit) return s def add_salt_noise(img): rows, cols, dims = img.shape R = np.mat(img[:, :, 0]) G = np.mat(img[:, :, 1]) B = np.mat(img[:, :, 2]) Grey_sp = R * 0.299 + G * 0.587 + B * 0.114 Grey_gs = R * 0.299 + G * 0.587 + B * 0.114 snr = 0.9 noise_num = int((1 - snr) * rows * cols) for i in range(noise_num): rand_x = random.randint(0, rows - 1) rand_y = random.randint(0, cols - 1) if random.randint(0, 1) == 0: Grey_sp[rand_x, rand_y] = 0 else: Grey_sp[rand_x, rand_y] = 255 #给图像加入高斯噪声 Grey_gs = Grey_gs + np.random.normal(0, 48, Grey_gs.shape) Grey_gs = Grey_gs - np.full(Grey_gs.shape, np.min(Grey_gs)) Grey_gs = Grey_gs * 255 / np.max(Grey_gs) Grey_gs = Grey_gs.astype(np.uint8) # 中值滤波 Grey_sp_mf = scipy.ndimage.median_filter(Grey_sp, (7, 7)) Grey_gs_mf = scipy.ndimage.median_filter(Grey_gs, (8, 8)) # 均值滤波 Grey_sp_me = convert_2d(Grey_sp) Grey_gs_me = convert_2d(Grey_gs) plt.subplot(321) plt.title('加入椒盐噪声',fontproperties=font_set) plt.imshow(Grey_sp, cmap='gray') plt.subplot(322) plt.title('加入高斯噪声',fontproperties=font_set) plt.imshow(Grey_gs, cmap='gray') plt.subplot(323) plt.title('中值滤波去椒盐噪声（8*8）',fontproperties=font_set) plt.imshow(Grey_sp_mf, cmap='gray') plt.subplot(324) plt.title('中值滤波去高斯噪声（8*8）',fontproperties=font_set) plt.imshow(Grey_gs_mf, cmap='gray') plt.subplot(325) plt.title('均值滤波去椒盐噪声',fontproperties=font_set) plt.imshow(Grey_sp_me, cmap='gray') plt.subplot(326) plt.title('均值滤波去高斯噪声',fontproperties=font_set) plt.imshow(Grey_gs_me, cmap='gray') plt.show() def main(): img = np.array(Image.open('E:/pycharm/GraduationDesign/Test/testthree.png')) add_salt_noise(img) if __name__ == '__main__': main()

效果如下

以上这篇Python实现图像去噪方式(中值去噪和均值去噪)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。