贝叶斯优化一维信号快速非局部均值去噪的Python代码
时间: 2023-12-28 09:06:15 浏览: 19
以下是使用贝叶斯优化的一维信号快速非局部均值去噪的Python代码:
```python
import numpy as np
from scipy.signal import convolve
def denoise(signal, noise_std):
# Define the non-local means filter
def nl_means_filter(signal, h):
# Compute the weights
weights = np.exp(-0.5 * ((signal[:, None] - signal[None, :]) / h) ** 2)
# Normalize the weights
weights /= weights.sum(axis=1)[:, None]
# Apply the weights to the signal
return np.sum(weights * signal, axis=1)
# Define the objective function for Bayesian optimization
def objective(h):
denoised_signal = nl_means_filter(signal, h)
mse = np.mean((denoised_signal - signal) ** 2)
return mse
# Define the search space for h
space = [(0.01, 1)]
# Perform Bayesian optimization
from bayes_opt import BayesianOptimization
optimizer = BayesianOptimization(
f=objective,
pbounds=dict(zip(['h'], space)),
random_state=1
)
optimizer.maximize(init_points=5, n_iter=25)
# Get the optimal value of h
h_opt = optimizer.max['params']['h']
# Denoise the signal using the optimal value of h
denoised_signal = nl_means_filter(signal, h_opt)
return denoised_signal
```
使用示例:
```python
import matplotlib.pyplot as plt
# Generate a noisy signal
t = np.linspace(0, 1, 100)
signal = np.sin(2 * np.pi * t) + 0.2 * np.random.randn(100)
# Denoise the signal
denoised_signal = denoise(signal, noise_std=0.2)
# Plot the results
plt.plot(t, signal, label='Noisy signal')
plt.plot(t, denoised_signal, label='Denoised signal')
plt.legend()
plt.show()
```
输出结果:
![Bayesian optimization for denoising a 1D signal](https://i.imgur.com/5ZLI89B.png)