五、编程题:读入一幅mri.tif图像，添加椒盐噪声，分别采用均值滤波和中值滤波进行处理，

首先，我们需要安装Python的图像处理库PIL和NumPy。可以使用以下命令安装： ```bash pip install pillow numpy ``` 接下来，我们将编写一个Python脚本来读取MRI图像，添加椒盐噪声，然后使用均值滤波和中值滤波进行处理。 ```python from PIL import Image import numpy as np # 读取MRI图像 def read_image(file_path): img = Image.open(file_path) return img # 添加椒盐噪声 def add_salt_pepper_noise(img, noise_ratio): img_array = np.array(img) height, width = img_array.shape num_noise_pixels = int(height * width * noise_ratio) for _ in range(num_noise_pixels): x = np.random.randint(0, width) y = np.random.randint(0, height) if np.random.random() < 0.5: img_array[y, x] = 0 else: img_array[y, x] = 255 return Image.fromarray(img_array) # 均值滤波 def mean_filter(img, kernel_size): img_array = np.array(img) height, width = img_array.shape pad = kernel_size // 2 output = np.zeros_like(img_array) for y in range(pad, height - pad): for x in range(pad, width - pad): window = img_array[y - pad:y + pad + 1, x - pad:x + pad + 1] output[y, x] = np.mean(window) return Image.fromarray(output) # 中值滤波 def median_filter(img, kernel_size): img_array = np.array(img) height, width = img_array.shape pad = kernel_size // 2 output = np.zeros_like(img_array) for y in range(pad, height - pad): for x in range(pad, width - pad): window = img_array[y - pad:y + pad + 1, x - pad:x + pad + 1] output[y, x] = np.median(window) return Image.fromarray(output) # 主函数 def main(): input_file = 'mri.tif' output_file_mean = 'mri_mean_filtered.tif' output_file_median = 'mri_median_filtered.tif' noise_ratio = 0.05 kernel_size = 3 img = read_image(input_file) img_with_noise = add_salt_pepper_noise(img, noise_ratio) img_mean_filtered = mean_filter(img_with_noise, kernel_size) img_median_filtered = median_filter(img_with_noise, kernel_size) img_mean_filtered.save(output_file_mean) img_median_filtered.save(output_file_median) if __name__ == '__main__': main() ``` 将上述代码保存为`mri_filtering.py`，然后在命令行中运行`python mri_filtering.py`。这将生成两个处理后的图像文件：`mri_mean_filtered.tif`和`mri_median_filtered.tif`。