2、高斯噪声(Gaussian Noise) 待更 二、图像卷积操作(Convolution) 三、图像滤波技术:去噪与平滑处理(Filtering) 1、均值滤波(Averaging Filtering) 2、高斯滤波(Gaussian Filtering) 3、中值滤波(Median Filtering) 4、双边滤波(Bilateral Filtering) 编辑于
noisy_image = add_gaussian_noise(image) # 显示结果 cv2.imshow('Noisy Image', noisy_image) cv2.waitKey(0) cv2.destroyAllWindows() ``` 2. 添加椒盐噪声 ```python def add_salt_and_pepper_noise(image, salt_prob=0.05, pepper_prob=0.05): noisy_image = np.copy(image) salt = np.random....
importnumpyasnp# 生成一个简单的信号t=np.linspace(0,10,100)signal=np.sin(t)# 添加高斯噪声noise=np.random.normal(loc=0,scale=0.1,size=signal.shape)noisy_signal=signal+noiseprint("numpyarray.com - Original signal:",signal[:10])print("numpyarray.com - Noisy signal:",noisy_signal[:10]) P...
Returns --- samples: :py:class:`ndarray <numpy.ndarray>` of shape `(n_ex, latent_dim)` """ # 生成服从标准正态分布的噪声 noise = np.random.normal(loc=0.0, scale=1.0, size=t_mean.shape) # 使用重参数化技巧从分布中抽样 samples = noise * np.exp(t_log_var) + t_mean # 保存抽样...
(policy, cmab, ep_length, n_episodes, n_duplicates) def plot_ucb1_gaussian_shortest_path(): """ Plot the UCB1 policy on a graph shortest path problem each edge weight drawn from an independent univariate Gaussian """ # 设置随机种子 np.random.seed(12345) ep_length = 1 n_duplicates ...
Gaussian mixture model EM training Hidden Markov model Viterbi decoding Likelihood computation MLE parameter estimation via Baum-Welch/forward-backward algorithm Latent Dirichlet allocation (topic model) Standard model with MLE parameter estimation via variational EM Smoothed model with MAP parameter estimation...
线性代数是数学的重要分支。numpy.linalg包包含线性代数函数。 使用此模块,您可以求矩阵求逆,计算特征值,求解线性方程式和确定行列式等。 实战时间 – 转换矩阵 线性代数中矩阵A的逆是矩阵A^(-1),当与原始矩阵相乘时,它等于单位矩阵I。 可以这样写:
Gaussian mixture model EM training Hidden Markov model Viterbi decoding Likelihood computation MLE parameter estimation via Baum-Welch/forward-backward algorithm Latent Dirichlet allocation(topic model) Standard model with MLE parameter estimation via variational EM ...
improved by lowering the polynomial degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter can also be set to a value smaller than its default, but the resulting fit may be spurious: including contributions from the small singular values can add numerical noise to the ...
Gaussian mixture model EM training Hidden Markov model Viterbi decoding Likelihood computation MLE parameter estimation via Baum-Welch/forward-backward algorithm Latent Dirichlet allocation (topic model) Standard model with MLE parameter estimation via variational EM ...