给出自适应均值滤波去除批量声呐图像噪声的python代码

当然可以！以下是一个使用自适应均值滤波器去除批量声呐图像噪声的Python代码示例：

import cv2
import numpy as np

def adaptive_mean_filter(image, window_size, threshold):
    filtered_image = np.zeros_like(image)
    pad_size = window_size // 2

    padded_image = cv2.copyMakeBorder(image, pad_size, pad_size, pad_size, pad_size, cv2.BORDER_REFLECT)

    for i in range(pad_size, padded_image.shape[0] - pad_size):
        for j in range(pad_size, padded_image.shape[1] - pad_size):
            window = padded_image[i - pad_size:i + pad_size + 1, j - pad_size:j + pad_size + 1]

            median = np.median(window)
            deviation = np.median(np.abs(window - median))

            if deviation > threshold:
                filtered_image[i - pad_size, j - pad_size] = median
            else:
                filtered_image[i - pad_size, j - pad_size] = padded_image[i, j]

    return filtered_image

# 读取图像
image = cv2.imread("noisy_image.png", 0)

# 设置窗口大小和阈值
window_size = 3
threshold = 10

# 应用自适应均值滤波器
filtered_image = adaptive_mean_filter(image, window_size, threshold)

# 显示结果
cv2.imshow("Original Image", image)
cv2.imshow("Filtered Image", filtered_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，此代码需要使用OpenCV库进行图像处理。要运行此代码，请确保已安装OpenCV库，并将待处理的图像文件名替换为`noisy_image.png`。您还可以根据需要调整窗口大小和阈值参数。