反卷积_上采样对比

最新推荐文章于 2025-07-29 10:07:30 发布

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最新推荐文章于 2025-07-29 10:07:30 发布 · 678 阅读

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#深度学习 #tensorflow

本文介绍了一种使用自编码器对MNIST手写数字数据集进行图像去噪的方法。通过构建卷积自编码器和上采样自编码器，实现了对加噪图像的有效恢复。实验结果显示，两种自编码器均能在保持较高准确率的同时降低损失，有效提升了图像质量。

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

#config = ConfigProto()
#config.gpu_options.allow_growth = True
#session = InteractiveSession(config=config)

(x_train,x_label),(y_train,y_label)=tf.keras.datasets.mnist.load_data()
x_train.shape,y_train.shape

((60000, 28, 28), (10000, 28, 28))

x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
x_train = x_train/255
y_train = y_train/255
x_train = np.reshape(x_train,(len(x_train),28,28,1))
y_train = np.reshape(y_train,(len(y_train),28,28,1))
y_train.shape

(10000, 28, 28, 1)

noise_factor = 0.5
noise_x = x_train + noise_factor*np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
noise_y = y_train + noise_factor*np.random.normal(loc=0.0, scale=1.0, size=y_train.shape)

n = 20
plt.figure(figsize=(60, 12))    # 指定宽为20，高为4
for i in range(1, 11):
    ax = plt.subplot(2, n/2, i)  # 表示将设置为2行，n/2列，当前位置在i。
    plt.imshow(x_train[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    ax = plt.subplot(2, n/2, 10+i)
    plt.imshow(noise_x[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

在这里插入图片描述

input = tf.keras.layers.Input(shape=(28,28,1))
x1 = tf.keras.layers.Conv2D(32,(3,3),padding='same',activation = 'relu',name = 'x1')(input) #变为28*28*32
x2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),strides=(2, 2),padding='same',name = 'x2')(x1) #变为14*14*32
x3 = tf.keras.layers.Conv2D(64,(3,3),padding='same',activation = 'relu',name = 'x3')(x2)#变为14*14*64
x4 = tf.keras.layers.MaxPooling2D