基于卷积神经网络的图像降噪pycharm最简单代码

基于卷积神经网络的图像降噪的代码比较复杂，需要涉及到神经网络的构建、训练和评估等过程。下面是一个使用PyCharm实现基于卷积神经网络的图像降噪的简单代码示例：

import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras.datasets import mnist

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

# 归一化数据
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# 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.)

# 构建卷积自编码器模型
input_img = Input(shape=(28, 28, 1))
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# 训练模型
autoencoder.fit(x_train_noisy, x_train,
                epochs=50,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test_noisy, x_test))

# 对测试集进行降噪处理
decoded_imgs = autoencoder.predict(x_test_noisy)

# 显示结果
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
    # 显示原始图像
    ax = plt.subplot(2, 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)

    # 显示降噪后的图像
    ax = plt.subplot(2, n, i + n + 1)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

需要注意的是，这个示例代码仅供参考，实际的应用中需要根据具体情况进行调整和完善。