{"id":1095637,"date":"2025-01-08T14:53:04","date_gmt":"2025-01-08T06:53:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1095637.html"},"modified":"2025-01-08T14:53:08","modified_gmt":"2025-01-08T06:53:08","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e7%81%b0%e5%ba%a6%e5%9b%be%e5%8e%bb%e5%99%aa-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1095637.html","title":{"rendered":"python\u5982\u4f55\u5c06\u7070\u5ea6\u56fe\u53bb\u566a"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u7070\u5ea6\u56fe\u53bb\u566a\"<\/p>\n

Python\u5982\u4f55\u5c06\u7070\u5ea6\u56fe\u53bb\u566a\uff1a<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528\u6ee4\u6ce2\u6280\u672f\u3001\u4f7f\u7528\u5f62\u6001\u5b66\u64cd\u4f5c\u3001\u4f7f\u7528\u5085\u91cc\u53f6\u53d8\u6362\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/strong>\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u6280\u672f\u6765\u5bf9\u7070\u5ea6\u56fe\u50cf\u8fdb\u884c\u53bb\u566a\u3002\u6211\u4eec\u5c06\u91cd\u70b9\u8ba8\u8bba\u4f7f\u7528\u6ee4\u6ce2\u6280\u672f<\/strong>\uff0c\u7279\u522b\u662f\u4e2d\u503c\u6ee4\u6ce2\u548c\u9ad8\u65af\u6ee4\u6ce2\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u6ee4\u6ce2\u6280\u672f<\/p>\n

\u6ee4\u6ce2\u662f\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5e38\u89c1\u65b9\u6cd5\uff0c\u7528\u4e8e\u5e73\u6ed1\u56fe\u50cf\u5e76\u53bb\u9664\u566a\u58f0\u3002\u5e38\u89c1\u7684\u6ee4\u6ce2\u65b9\u6cd5\u5305\u62ec\u5747\u503c\u6ee4\u6ce2\u3001\u4e2d\u503c\u6ee4\u6ce2\u548c\u9ad8\u65af\u6ee4\u6ce2\u3002<\/p>\n<\/p>\n

1.1\u3001\u5747\u503c\u6ee4\u6ce2<\/p>\n

\u5747\u503c\u6ee4\u6ce2\u662f\u4e00\u79cd\u7b80\u5355\u7684\u56fe\u50cf\u5e73\u6ed1\u6280\u672f\uff0c\u901a\u8fc7\u5c06\u50cf\u7d20\u503c\u66ff\u6362\u4e3a\u5176\u90bb\u57df\u5185\u6240\u6709\u50cf\u7d20\u503c\u7684\u5e73\u5747\u503c\u6765\u53bb\u9664\u566a\u58f0\u3002\u5b83\u53ef\u4ee5\u6709\u6548\u5730\u51cf\u5c11\u56fe\u50cf\u4e2d\u7684\u9ad8\u9891\u566a\u58f0\uff0c\u4f46\u53ef\u80fd\u4f1a\u5bfc\u81f4\u56fe\u50cf\u8fb9\u7f18\u7684\u6a21\u7cca\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u5e94\u7528\u5747\u503c\u6ee4\u6ce2<\/strong><\/h2>\n
kernel_size = 5<\/p>\n
blurred_image = cv2.blur(image, (kernel_size, kernel_size))<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Blurred Image', blurred_image)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u4e2d\u503c\u6ee4\u6ce2<\/p>\n
\u4e2d\u503c\u6ee4\u6ce2\u662f\u4e00\u79cd\u975e\u7ebf\u6027\u6ee4\u6ce2\u65b9\u6cd5\uff0c\u5b83\u4f7f\u7528\u90bb\u57df\u5185\u50cf\u7d20\u503c\u7684\u4e2d\u503c\u6765\u66ff\u6362\u4e2d\u5fc3\u50cf\u7d20\u503c\u3002\u4e0e\u5747\u503c\u6ee4\u6ce2\u76f8\u6bd4\uff0c\u4e2d\u503c\u6ee4\u6ce2\u5728\u4fdd\u7559\u56fe\u50cf\u8fb9\u7f18\u7ec6\u8282\u65b9\u9762\u8868\u73b0\u66f4\u597d\uff0c\u662f\u53bb\u9664\u6912\u76d0\u566a\u58f0\u7684\u6709\u6548\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u4e2d\u503c\u6ee4\u6ce2\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u5e94\u7528\u4e2d\u503c\u6ee4\u6ce2<\/strong><\/h2>\n
kernel_size = 5<\/p>\n
blurred_image = cv2.medianBlur(image, kernel_size)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Median Blurred Image', blurred_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3\u3001\u9ad8\u65af\u6ee4\u6ce2<\/p>\n
\u9ad8\u65af\u6ee4\u6ce2\u662f\u4e00\u79cd\u57fa\u4e8e\u9ad8\u65af\u51fd\u6570\u7684\u7ebf\u6027\u6ee4\u6ce2\u65b9\u6cd5\uff0c\u901a\u8fc7\u5377\u79ef\u64cd\u4f5c\u6765\u5e73\u6ed1\u56fe\u50cf\u3002\u5b83\u53ef\u4ee5\u6709\u6548\u5730\u51cf\u5c11\u9ad8\u9891\u566a\u58f0\uff0c\u540c\u65f6\u4fdd\u7559\u56fe\u50cf\u7684\u8fb9\u7f18\u7ec6\u8282\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u9ad8\u65af\u6ee4\u6ce2\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u5e94\u7528\u9ad8\u65af\u6ee4\u6ce2<\/strong><\/h2>\n
kernel_size = 5<\/p>\n
sigma = 1.0<\/p>\n
blurred_image = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Gaussian Blurred Image', blurred_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u5f62\u6001\u5b66\u64cd\u4f5c<\/p>\n
\u5f62\u6001\u5b66\u64cd\u4f5c\u662f\u4e00\u79cd\u57fa\u4e8e\u56fe\u50cf\u5f62\u72b6\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u5e38\u7528\u4e8e\u53bb\u9664\u566a\u58f0\u3001\u5206\u5272\u56fe\u50cf\u548c\u63d0\u53d6\u56fe\u50cf\u7279\u5f81\u3002\u5e38\u89c1\u7684\u5f62\u6001\u5b66\u64cd\u4f5c\u5305\u62ec\u81a8\u80c0\u3001\u8150\u8680\u3001\u5f00\u8fd0\u7b97\u548c\u95ed\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
2.1\u3001\u81a8\u80c0\u548c\u8150\u8680<\/p>\n
\u81a8\u80c0\u548c\u8150\u8680\u662f\u5f62\u6001\u5b66\u64cd\u4f5c\u4e2d\u7684\u57fa\u672c\u64cd\u4f5c\u3002\u81a8\u80c0\u64cd\u4f5c\u901a\u8fc7\u5c06\u50cf\u7d20\u503c\u66ff\u6362\u4e3a\u5176\u90bb\u57df\u5185\u7684\u6700\u5927\u503c\u6765\u589e\u52a0\u56fe\u50cf\u7684\u4eae\u5ea6\u533a\u57df\uff0c\u800c\u8150\u8680\u64cd\u4f5c\u5219\u901a\u8fc7\u5c06\u50cf\u7d20\u503c\u66ff\u6362\u4e3a\u5176\u90bb\u57df\u5185\u7684\u6700\u5c0f\u503c\u6765\u51cf\u5c11\u56fe\u50cf\u7684\u4eae\u5ea6\u533a\u57df\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u81a8\u80c0\u548c\u8150\u8680\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u5b9a\u4e49\u7ed3\u6784\u5143\u7d20<\/strong><\/h2>\n
kernel = np.ones((5, 5), np.uint8)<\/p>\n
\u5e94\u7528\u81a8\u80c0\u64cd\u4f5c<\/strong><\/h2>\n
dilated_image = cv2.dilate(image, kernel, iterations=1)<\/p>\n
\u5e94\u7528\u8150\u8680\u64cd\u4f5c<\/strong><\/h2>\n
eroded_image = cv2.erode(image, kernel, iterations=1)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Dilated Image', dilated_image)<\/p>\n
cv2.imshow('Eroded Image', eroded_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u5f00\u8fd0\u7b97\u548c\u95ed\u8fd0\u7b97<\/p>\n
\u5f00\u8fd0\u7b97\u662f\u5148\u8150\u8680\u540e\u81a8\u80c0\u7684\u7ec4\u5408\u64cd\u4f5c\uff0c\u7528\u4e8e\u53bb\u9664\u5c0f\u7684\u566a\u58f0\u70b9\u3002\u95ed\u8fd0\u7b97\u662f\u5148\u81a8\u80c0\u540e\u8150\u8680\u7684\u7ec4\u5408\u64cd\u4f5c\uff0c\u7528\u4e8e\u586b\u8865\u5c0f\u7684\u9ed1\u8272\u533a\u57df\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u5f00\u8fd0\u7b97\u548c\u95ed\u8fd0\u7b97\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u5b9a\u4e49\u7ed3\u6784\u5143\u7d20<\/strong><\/h2>\n
kernel = np.ones((5, 5), np.uint8)<\/p>\n
\u5e94\u7528\u5f00\u8fd0\u7b97<\/strong><\/h2>\n
opening_image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)<\/p>\n
\u5e94\u7528\u95ed\u8fd0\u7b97<\/strong><\/h2>\n
closing_image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Opening Image', opening_image)<\/p>\n
cv2.imshow('Closing Image', closing_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5085\u91cc\u53f6\u53d8\u6362<\/p>\n
\u5085\u91cc\u53f6\u53d8\u6362\u662f\u4e00\u79cd\u5c06\u56fe\u50cf\u4ece\u7a7a\u95f4\u57df\u8f6c\u6362\u5230\u9891\u7387\u57df\u7684\u6570\u5b66\u53d8\u6362\uff0c\u7528\u4e8e\u5206\u6790\u56fe\u50cf\u4e2d\u7684\u9891\u7387\u6210\u5206\u3002\u5728\u9891\u7387\u57df\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u6ee4\u9664\u9ad8\u9891\u5206\u91cf\u6765\u53bb\u9664\u566a\u58f0\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u5b9e\u73b0\u5085\u91cc\u53f6\u53d8\u6362\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u8fdb\u884c\u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/h2>\n
dft = cv2.dft(np.float32(image), flags=cv2.DFT_COMPLEX_OUTPUT)<\/p>\n
dft_shift = np.fft.fftshift(dft)<\/p>\n
\u6784\u5efa\u9891\u7387\u63a9\u7801<\/strong><\/h2>\n
rows, cols = image.shape<\/p>\n
crow, ccol = rows \/\/ 2 , cols \/\/ 2<\/p>\n
mask = np.zeros((rows, cols, 2), np.uint8)<\/p>\n
mask[crow-30:crow+30, ccol-30:ccol+30] = 1<\/p>\n
\u5e94\u7528\u9891\u7387\u63a9\u7801<\/strong><\/h2>\n
fshift = dft_shift * mask<\/p>\n
\u8fdb\u884c\u9006\u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/h2>\n
f_ishift = np.fft.ifftshift(fshift)<\/p>\n
img_back = cv2.idft(f_ishift)<\/p>\n
img_back = cv2.magnitude(img_back[:,:,0], img_back[:,:,1])<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Filtered Image', img_back)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/p>\n
\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u7279\u522b\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\uff0c\u5728\u56fe\u50cf\u53bb\u566a\u65b9\u9762\u8868\u73b0\u51fa\u4e86\u5353\u8d8a\u7684\u6027\u80fd\u3002\u901a\u8fc7\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u80fd\u591f\u6709\u6548\u5730\u5b66\u4e60\u548c\u53bb\u9664\u5404\u79cd\u7c7b\u578b\u7684\u566a\u58f0\u3002<\/p>\n<\/p>\n
4.1\u3001\u51c6\u5907\u6570\u636e\u96c6<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u51c6\u5907\u4e00\u4e2a\u5305\u542b\u5e26\u566a\u58f0\u56fe\u50cf\u548c\u5bf9\u5e94\u65e0\u566a\u58f0\u56fe\u50cf\u7684\u6570\u636e\u96c6\u3002\u53ef\u4ee5\u4f7f\u7528\u73b0\u6709\u7684\u6570\u636e\u96c6\uff0c\u6216\u8005\u901a\u8fc7\u5411\u56fe\u50cf\u6dfb\u52a0\u4eba\u5de5\u566a\u58f0\u6765\u751f\u6210\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
4.2\u3001\u6784\u5efa\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Keras\u6216PyTorch\u7b49\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u6765\u6784\u5efa\u6a21\u578b\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u4f7f\u7528Keras\u6784\u5efa\u7b80\u5355\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import cv2<\/p>\n
import os<\/p>\n
from keras.models import Sequential<\/p>\n
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D<\/p>\n
from keras.preprocessing.image import img_to_array, load_img<\/p>\n
\u52a0\u8f7d\u6570\u636e\u96c6<\/strong><\/h2>\n
def load_data(data_dir):<\/p>\n
    images = []<\/p>\n
    for filename in os.listdir(data_dir):<\/p>\n
        img = load_img(os.path.join(data_dir, filename), color_mode='grayscale')<\/p>\n
        img = img_to_array(img)<\/p>\n
        images.append(img)<\/p>\n
    return np.array(images)<\/p>\n
\u6784\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = Sequential()<\/p>\n
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(256, 256, 1)))<\/p>\n
model.add(MaxPooling2D((2, 2), padding='same'))<\/p>\n
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))<\/p>\n
model.add(MaxPooling2D((2, 2), padding='same'))<\/p>\n
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))<\/p>\n
model.add(UpSampling2D((2, 2)))<\/p>\n
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))<\/p>\n
model.add(UpSampling2D((2, 2)))<\/p>\n
model.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))<\/p>\n
model.compile(optimizer='adam', loss='binary_crossentropy')<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
train_data = load_data('train_data_dir')<\/p>\n
train_labels = load_data('train_labels_dir')<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(train_data, train_labels, epochs=50, batch_size=64, validation_split=0.2)<\/p>\n
\u4fdd\u5b58\u6a21\u578b<\/strong><\/h2>\n
model.save('denoising_model.h5')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3\u3001\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u53bb\u566a<\/p>\n
\u5728\u8bad\u7ec3\u597d\u6a21\u578b\u4e4b\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u5bf9\u5e26\u566a\u58f0\u7684\u56fe\u50cf\u8fdb\u884c\u53bb\u566a\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import cv2<\/p>\n
from keras.models import load_model<\/p>\n
from keras.preprocessing.image import img_to_array, array_to_img<\/p>\n
\u52a0\u8f7d\u6a21\u578b<\/strong><\/h2>\n
model = load_model('denoising_model.h5')<\/p>\n
\u52a0\u8f7d\u5e26\u566a\u58f0\u7684\u56fe\u50cf<\/strong><\/h2>\n
img = load_img('noisy_image.jpg', color_mode='grayscale')<\/p>\n
img = img_to_array(img)<\/p>\n
img = np.expand_dims(img, axis=0)<\/p>\n
\u8fdb\u884c\u53bb\u566a<\/strong><\/h2>\n
denoised_img = model.predict(img)<\/p>\n
denoised_img = np.squeeze(denoised_img, axis=0)<\/p>\n
denoised_img = array_to_img(denoised_img)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
denoised_img.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3\uff1a<\/p>\n
\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u6ee4\u6ce2\u6280\u672f\u3001\u5f62\u6001\u5b66\u64cd\u4f5c\u3001\u5085\u91cc\u53f6\u53d8\u6362\u548c\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6765\u5bf9\u7070\u5ea6\u56fe\u50cf\u8fdb\u884c\u53bb\u566a\u3002\u6ee4\u6ce2\u6280\u672f<\/strong>\uff08\u5c24\u5176\u662f\u4e2d\u503c\u6ee4\u6ce2\u548c\u9ad8\u65af\u6ee4\u6ce2\uff09\u662f\u53bb\u566a\u7684\u5e38\u89c1\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\u3002\u5f62\u6001\u5b66\u64cd\u4f5c<\/strong>\u53ef\u4ee5\u6709\u6548\u53bb\u9664\u7279\u5b9a\u7c7b\u578b\u7684\u566a\u58f0\uff0c\u5982\u6912\u76d0\u566a\u58f0\u3002\u5085\u91cc\u53f6\u53d8\u6362<\/strong>\u901a\u8fc7\u9891\u7387\u57df\u5904\u7406\u6765\u53bb\u9664\u9ad8\u9891\u566a\u58f0\u3002\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/strong>\uff0c\u7279\u522b\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\uff0c\u5728\u56fe\u50cf\u53bb\u566a\u65b9\u9762\u8868\u73b0\u51fa\u8272\uff0c\u9002\u7528\u4e8e\u590d\u6742\u566a\u58f0\u7684\u53bb\u9664\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u566a\u58f0\u7c7b\u578b\u3002\u901a\u8fc7\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u597d\u7684\u53bb\u566a\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684\u5e93\u8fdb\u884c\u7070\u5ea6\u56fe\u53bb\u566a\u5904\u7406\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53bb\u566a\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u5e93\u5b9e\u73b0\uff0c\u4f8b\u5982OpenCV\u3001scikit-image\u548cPIL\u3002\u4f7f\u7528OpenCV\u4e2d\u7684cv2.GaussianBlur()<\/code>\u53ef\u4ee5\u6709\u6548\u5730\u5e73\u6ed1\u56fe\u50cf\uff0c\u51cf\u5c11\u566a\u58f0\uff0c\u540c\u65f6\u4fdd\u7559\u8fb9\u7f18\u7279\u5f81\u3002\u53e6\u4e00\u4e2a\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u5229\u7528skimage.restoration<\/code>\u6a21\u5757\u4e2d\u7684denoise_tv_chambolle<\/code>\u51fd\u6570\uff0c\u8be5\u65b9\u6cd5\u57fa\u4e8e\u603b\u53d8\u5dee\uff08Total Variation\uff09\u53bb\u566a\uff0c\u80fd\u591f\u8f83\u597d\u5730\u4fdd\u7559\u7ec6\u8282\u3002<\/p>\n
\u53bb\u566a\u8fc7\u7a0b\u4e2d\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u53c2\u6570\u8bbe\u7f6e\uff1f<\/strong>
\u53bb\u566a\u7684\u6548\u679c\u5f80\u5f80\u4e0e\u6240\u4f7f\u7528\u65b9\u6cd5\u7684\u53c2\u6570\u5bc6\u5207\u76f8\u5173\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u9ad8\u65af\u6a21\u7cca\u65f6\uff0c\u6a21\u7cca\u6838\u7684\u5927\u5c0f\u76f4\u63a5\u5f71\u54cd\u53bb\u566a\u6548\u679c\uff0c\u8fc7\u5927\u7684\u6838\u4f1a\u5bfc\u81f4\u56fe\u50cf\u6a21\u7cca\uff0c\u800c\u8fc7\u5c0f\u5219\u53ef\u80fd\u65e0\u6cd5\u6709\u6548\u53bb\u566a\u3002\u5bf9\u4e8e\u603b\u53d8\u5dee\u53bb\u566a\uff0cweight<\/code>\u53c2\u6570\u63a7\u5236\u53bb\u566a\u5f3a\u5ea6\uff0c\u8c03\u6574\u8be5\u503c\u53ef\u4ee5\u5728\u4fdd\u7559\u56fe\u50cf\u7ec6\u8282\u548c\u53bb\u9664\u566a\u58f0\u4e4b\u95f4\u627e\u5230\u5e73\u8861\u3002<\/p>\n
\u5982\u4f55\u8bc4\u4f30\u53bb\u566a\u540e\u7684\u56fe\u50cf\u8d28\u91cf\uff1f<\/strong>
\u53bb\u566a\u540e\u7684\u56fe\u50cf\u8d28\u91cf\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8bc4\u4f30\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u8ba1\u7b97\u5cf0\u503c\u4fe1\u566a\u6bd4\uff08PSNR\uff09\u548c\u7ed3\u6784\u76f8\u4f3c\u6027\u6307\u6570\uff08SSIM\uff09\u3002PSNR\u503c\u8d8a\u9ad8\uff0c\u56fe\u50cf\u8d28\u91cf\u8d8a\u597d\uff1bSSIM\u5219\u7528\u6765\u8861\u91cf\u4e24\u5e45\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\uff0c\u901a\u5e38\u5728\u53bb\u566a\u4efb\u52a1\u4e2d\uff0cSSIM\u7684\u503c\u8d8a\u63a5\u8fd11\uff0c\u8868\u793a\u53bb\u566a\u6548\u679c\u8d8a\u597d\u3002\u6b64\u5916\uff0c\u89c6\u89c9\u8bc4\u4f30\u4e5f\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6807\u51c6\uff0c\u901a\u8fc7\u89c2\u5bdf\u53bb\u566a\u524d\u540e\u7684\u56fe\u50cf\u5bf9\u6bd4\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5224\u65ad\u53bb\u566a\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5982\u4f55\u5c06\u7070\u5ea6\u56fe\u53bb\u566a\uff1a \u4f7f\u7528\u6ee4\u6ce2\u6280\u672f\u3001\u4f7f\u7528\u5f62\u6001\u5b66\u64cd\u4f5c\u3001\u4f7f\u7528\u5085\u91cc\u53f6\u53d8\u6362\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c […]","protected":false},"author":3,"featured_media":1095647,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1095637"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1095637"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1095637\/revisions"}],"predecessor-version":[{"id":1095648,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1095637\/revisions\/1095648"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1095647"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1095637"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1095637"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1095637"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}