vivado图像去躁代码

Vivado是Xilinx公司的FPGA开发工具，不是用于图像去噪的工具。如果你想要实现图像去噪，可以使用深度学习等技术，例如卷积神经网络（CNN）。以下是一个简单的图像去噪CNN的Python代码示例：

import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from keras.callbacks import TensorBoard
from keras.datasets import mnist

# 加载数据集
(x_train, _), (x_test, _) = mnist.load_data()

# 归一化和预处理
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

# 添加噪声
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)

# 定义模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)))
model.add(MaxPooling2D((2, 2), padding='same'))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((2, 2), padding='same'))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))

# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy')

# 训练模型
history = model.fit(x_train_noisy, x_train, epochs=10, batch_size=128, shuffle=True,
                    validation_data=(x_test_noisy, x_test),
                    callbacks=[TensorBoard(log_dir='/tmp/tb', histogram_freq=0, write_graph=False)])

# 使用模型去噪
denoised_imgs = model.predict(x_test_noisy)

# 展示结果
import matplotlib.pyplot as plt

n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
    # 原图
    ax = plt.subplot(3, n, i + 1)
    plt.imshow(x_test_noisy[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # 去噪后图
    ax = plt.subplot(3, n, i + 1 + n)
    plt.imshow(denoised_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # 原图
    ax = plt.subplot(3, n, i + 1 + 2 * n)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

这段代码使用MNIST数据集生成噪声图像，并使用CNN进行去噪。你可以根据自己的需求修改模型结构、超参数等，来实现更好的去噪效果。