首先,使用如Fig. 1(a)所示的生物特征密钥对三维医学图像进行加密,生成密文图像。然后,随机选取Fig. 1(a)中任一像素点的任一比特位进行反转,生成新密钥。新密钥用于解密密文图像,获得解密图像。最后,计算明文图像与解密图像之间的NPCR和UACI值。重复实验100次,计算NPCR和UACI的平均值,列在Table 10中。
时间: 2024-04-20 18:25:24 浏览: 13
First, the three-dimensional medical image is encrypted using the biometric key as shown in Figure 1(a), resulting in a ciphertext image. Next, a random bit of a pixel in Figure 1(a) is selected and inverted to generate a new key. This new key is then used to decrypt the ciphertext image, obtaining the decrypted image. Finally, the NPCR and UACI values between the original image and the decrypted image are calculated. The experiment is repeated 100 times, and the average NPCR and UACI values are listed in Table 10.
相关问题
使用matplotlib库生成图像的三维频谱代码:
以下是使用matplotlib库生成图像的三维频谱代码:
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
# Generate X, Y, Z coordinates for frequencies
fmax = 10
n = 100
X, Y = np.meshgrid(np.linspace(-fmax, fmax, n), np.linspace(-fmax, fmax, n))
Z = np.sqrt(X**2 + Y**2)
# Generate colormap and plot surface
surf = ax.plot_surface(X, Y, Z, cmap='magma', linewidth=0, antialiased=False)
ax.set_xlabel('Frequency X')
ax.set_ylabel('Frequency Y')
ax.set_zlabel('Amplitude')
ax.set_title('3D Frequency Spectrum')
# Add colorbar
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
```
希望这可以帮到您!
打开医学图像,使用Radon生成雷登变换图(正弦图),并使用iradon进行重建
好的,这里提供一个Python的示例代码,需要安装NumPy和matplotlib库:
```python
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import radon, iradon
# 生成一个圆形模板
N = 128
R = 40
x, y = np.indices((N, N))
mask = ((x-N//2)**2 + (y-N//2)**2) < R**2
# 生成雷登变换图
theta = np.linspace(0, 180, 180, endpoint=False)
sinogram = radon(mask.astype(float), theta=theta, circle=True)
# 显示正弦图
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))
ax1.imshow(mask, cmap='gray')
ax1.set_title('Original')
ax2.imshow(sinogram, cmap='gray', extent=(-90, 90, 0, sinogram.shape[0]), aspect='auto')
ax2.set_title('Sinogram')
plt.show()
# 重建图像
reconstruction = iradon(sinogram, theta=theta, circle=True)
reconstruction *= np.pi / (2. * len(theta)) # 标准化
error = np.sqrt(np.mean((mask - reconstruction) ** 2))
# 显示重建图像和误差
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))
ax1.imshow(mask, cmap='gray')
ax1.set_title('Original')
ax2.imshow(reconstruction, cmap='gray')
ax2.set_title('Reconstruction\nRMSE: %.3g' % error)
plt.show()
```
这个示例代码生成一个直径为80的圆形模板,并使用Radon函数生成180个角度下的正弦图像,然后使用iradon函数进行重建。你可以根据自己的需求修改代码中的参数。