完成高斯滤波、反谐波均值滤波、自适应中值滤波

adaptive_median_filter.py

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+import numpy as np
+
+# 自适应中值滤波
+def adaptive_median_filter(image, max_window_size=7):
+    padded_image = np.pad(image, max_window_size // 2, mode='edge')
+    filtered_image = image.copy()
+    height, width = image.shape
+
+    for i in range(height):
+        for j in range(width):
+            window_size = 3
+            while True:
+                half = window_size // 2
+                i1, i2 = i + max_window_size//2 - half, i + max_window_size//2 + half + 1
+                j1, j2 = j + max_window_size//2 - half, j + max_window_size//2 + half + 1
+                window = padded_image[i1:i2, j1:j2].flatten()
+                window = np.sort(window)
+
+                Z_min = window[0]
+                Z_max = window[-1]
+                Z_med = window[len(window) // 2]
+                Z_xy = padded_image[i + max_window_size//2, j + max_window_size//2]
+
+                A1 = Z_med - Z_min
+                A2 = Z_med - Z_max
+
+                if A1 > 0 and A2 < 0:  # Level A
+                    B1 = Z_xy - Z_min
+                    B2 = Z_xy - Z_max
+                    if B1 > 0 and B2 < 0:
+                        filtered_image[i, j] = Z_xy
+                    else:
+                        filtered_image[i, j] = Z_med
+                    break
+                else:
+                    window_size += 2
+                    if window_size > max_window_size:
+                        filtered_image[i, j] = Z_med
+                        break
+
+    return filtered_image

contraharmonic_mean_filter.py

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+import numpy as np
+
+#反谐波均值滤波
+def contraharmonic_mean_filter(img, kernel_size=3, Q=1.5):
+    img = img.astype(np.float64)
+    filtered_img = np.zeros_like(img)
+
+    k = kernel_size // 2
+    height, width = img.shape
+
+    for i in range(k, height - k):
+        for j in range(k, width - k):
+            window = img[i - k:i + k + 1, j - k:j + k + 1]
+            numerator = np.power(window, Q + 1).sum()
+            denominator = np.power(window, Q).sum()
+
+            if denominator != 0:
+                filtered_img[i, j] = numerator / denominator
+            else:
+                filtered_img[i, j] = img[i, j]
+
+    return np.clip(filtered_img, 0, 255).astype(np.uint8)

gaussian.py

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+import cv2
+import matplotlib.pyplot as plt
+plt.rcParams['font.sans-serif'] = ['simHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+#读取照片
+image_gaussian = cv2.imread("file/circuitboard-gaussian.tif")
+
+#高斯滤波
+kernel_size_gaussian = (5,5)    #核的大小
+sigmax = 0    #标准差
+image_gaussian2 = cv2.GaussianBlur(image_gaussian,kernel_size_gaussian,sigmax)
+
+plt.subplot(1,2,1)
+plt.imshow(image_gaussian, cmap='gray')
+plt.title('1.高斯噪声')
+plt.axis('off')
+
+plt.subplot(1,2,2)
+plt.imshow(image_gaussian2, cmap='gray')
+plt.title('1.高斯滤波_高斯噪声')
+plt.axis('off')
+
+plt.tight_layout()
+plt.show()

pepper_and_salt.py

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+import cv2
+import matplotlib.pyplot as plt
+plt.rcParams['font.sans-serif'] = ['simHei']
+plt.rcParams['axes.unicode_minus'] = False
+from adaptive_median_filter import adaptive_median_filter
+
+#读取照片
+image_saltandpep = cv2.imread("file/circuitboard-saltandpep.tif", cv2.IMREAD_GRAYSCALE)
+
+# 自适应中值滤波
+image_saltandpep2 = adaptive_median_filter(image_saltandpep)
+
+plt.subplot(1,2,1)
+plt.imshow(image_saltandpep, cmap='gray')
+plt.title('3.胡椒噪声和盐噪声')
+plt.axis('off')
+
+plt.subplot(1,2,2)
+plt.imshow(image_saltandpep2, cmap='gray')
+plt.title('3.自适应中值滤波_胡椒噪声和盐噪声')
+plt.axis('off')
+
+plt.tight_layout()
+plt.show()

pepper_pep.py

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+import cv2
+import matplotlib.pyplot as plt
+plt.rcParams['font.sans-serif'] = ['simHei']
+plt.rcParams['axes.unicode_minus'] = False
+from contraharmonic_mean_filter import contraharmonic_mean_filter
+
+#读取照片
+image_pepper = cv2.imread("file/circuitboard-pepper.tif", cv2.IMREAD_GRAYSCALE)
+image_salt = cv2.imread("file/circuitboard-salt.tif", cv2.IMREAD_GRAYSCALE)
+
+#反谐波均值滤波_胡椒噪声
+kernel_size_pepper = 3    #核的大小
+Q_pepper = 1.5    #滤波器阶数
+image_pepper2 = contraharmonic_mean_filter(image_pepper,kernel_size_pepper,Q_pepper)
+
+#反谐波均值滤波_盐噪声
+kernel_size_salt = 3    #核的大小
+Q_salt = -1.5    #滤波器阶数
+image_salt2 = contraharmonic_mean_filter(image_salt,kernel_size_salt,Q_salt)
+
+plt.subplot(2,2,1)
+plt.imshow(image_pepper, cmap='gray')
+plt.title('2.胡椒噪声')
+plt.axis('off')
+
+plt.subplot(2,2,2)
+plt.imshow(image_pepper2, cmap='gray')
+plt.title('2.反谐波均值滤波_胡椒噪声')
+plt.axis('off')
+
+plt.subplot(2,2,3)
+plt.imshow(image_salt, cmap='gray')
+plt.title('2.盐噪声')
+plt.axis('off')
+
+plt.subplot(2,2,4)
+plt.imshow(image_salt2, cmap='gray')
+plt.title('2.反谐波均值滤波_盐噪声')
+plt.axis('off')
+
+plt.tight_layout()
+plt.show()