深度学习中给图像去噪代码

深度学习在图像去噪通常使用卷积神经网络 (Convolutional Neural Networks, CNN) 或者更先进的模型，比如自编码器 (Autoencoder) 或生成对抗网络 (Generative Adversarial Networks, GANs)。这里是一个简单的Python示例，使用Keras库的U-Net架构进行图像去噪：

import keras
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from keras.models import Model

# 设定输入尺寸和噪声级别
input_shape = (256, 256, 3)
noise_level = 0.1

def build_unet(input_shape):
    inputs = Input(shape=input_shape)

    # 编码部分
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    # 隐藏层
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    # 中心层
    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool3)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    # 解码部分
    up5 = Conv2D(256, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop4))
    merge5 = concatenate([conv3, up5], axis=3)
    conv5 = Conv2D(256, (3, 3), activation='relu', padding='same')(merge5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(128, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop5))
    merge6 = concatenate([conv2, up6], axis=3)
    conv6 = Conv2D(128, (3, 3), activation='relu', padding='same')(merge6)
    drop6 = Dropout(0.5)(conv6)

    up7 = Conv2D(64, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop6))
    merge7 = concatenate([conv1, up7], axis=3)
    conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(merge7)
    drop7 = Dropout(0.5)(conv7)

    # 输出层
    output = Conv2D(3, (1, 1), activation='sigmoid')(conv7)

    model = Model(inputs=[inputs], outputs=[output])
    return model

unet = build_unet(input_shape)

这个代码创建了一个基本的U-Net模型，用于从包含噪声的图像中恢复清晰图像。训练模型需要大量的噪声图像对数据，并通过反向传播优化来调整权重。