用于单图像超分辨率的增强深度残差网络
作者：Gitesh Chawda
创建日期 2022/04/07
最后修改日期 2022/04/07
描述：在 DIV2K 数据集上训练 EDSR 模型。
简介
在本示例中，我们实现了 用于单图像超分辨率的增强深度残差网络 (EDSR)，作者是 Bee Lim、Sanghyun Son、Heewon Kim、Seungjun Nah 和 Kyoung Mu Lee。
EDSR 架构基于 SRResNet 架构，包含多个残差块。它使用恒定缩放层而不是批归一化层来产生一致的结果（输入和输出具有相似的分布，因此归一化中间特征可能不可取）。作者没有使用 L2 损失（均方误差），而是采用了 L1 损失（平均绝对误差），在经验上表现更好。
我们的实现仅包含 16 个具有 64 个通道的残差块。
或者，如 Keras 示例 使用高效的亚像素 CNN 进行图像超分辨率 中所示，可以使用 ESPCN 模型进行超分辨率。根据调查论文，EDSR 是基于 PSNR 分数的五个性能最佳的超分辨率方法之一。但是，它比其他方法具有更多参数，并且需要更多的计算能力。它的 PSNR 值（≈34db）略高于 ESPCN（≈32db）。根据调查论文，EDSR 的性能优于 ESPCN。
比较图表：
导入
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
AUTOTUNE = tf.data.AUTOTUNE
下载训练数据集
我们使用 DIV2K 数据集，这是一个著名的单图像超分辨率数据集，包含 1000 张具有各种降级类型的场景图像，分为 800 张用于训练，100 张用于验证，100 张用于测试。我们使用 4 倍双三次降采样图像作为我们的“低质量”参考。
# Download DIV2K from TF Datasets
# Using bicubic 4x degradation type
div2k_data = tfds.image.Div2k(config="bicubic_x4")
div2k_data.download_and_prepare()
# Taking train data from div2k_data object
train = div2k_data.as_dataset(split="train", as_supervised=True)
train_cache = train.cache()
# Validation data
val = div2k_data.as_dataset(split="validation", as_supervised=True)
val_cache = val.cache()
翻转、裁剪和调整图像大小
def flip_left_right(lowres_img, highres_img):
"""Flips Images to left and right."""
# Outputs random values from a uniform distribution in between 0 to 1
rn = tf.random.uniform(shape=(), maxval=1)
# If rn is less than 0.5 it returns original lowres_img and highres_img
# If rn is greater than 0.5 it returns flipped image
return tf.cond(
rn < 0.5,
lambda: (lowres_img, highres_img),
lambda: (
tf.image.flip_left_right(lowres_img),
tf.image.flip_left_right(highres_img),
),
)
def random_rotate(lowres_img, highres_img):
"""Rotates Images by 90 degrees."""
# Outputs random values from uniform distribution in between 0 to 4
rn = tf.random.uniform(shape=(), maxval=4, dtype=tf.int32)
# Here rn signifies number of times the image(s) are rotated by 90 degrees
return tf.image.rot90(lowres_img, rn), tf.image.rot90(highres_img, rn)
def random_crop(lowres_img, highres_img, hr_crop_size=96, scale=4):
"""Crop images.
low resolution images: 24x24
high resolution images: 96x96
"""
lowres_crop_size = hr_crop_size // scale # 96//4=24
lowres_img_shape = tf.shape(lowres_img)[:2] # (height,width)
lowres_width = tf.random.uniform(
shape=(), maxval=lowres_img_shape[1] - lowres_crop_size + 1, dtype=tf.int32
)
lowres_height = tf.random.uniform(
shape=(), maxval=lowres_img_shape[0] - lowres_crop_size + 1, dtype=tf.int32
)
highres_width = lowres_width * scale
highres_height = lowres_height * scale
lowres_img_cropped = lowres_img[
lowres_height : lowres_height + lowres_crop_size,
lowres_width : lowres_width + lowres_crop_size,
] # 24x24
highres_img_cropped = highres_img[
highres_height : highres_height + hr_crop_size,
highres_width : highres_width + hr_crop_size,
] # 96x96
return lowres_img_cropped, highres_img_cropped
准备一个 tf.data.Dataset 对象
我们使用随机水平翻转和 90 度旋转来增强训练数据。
作为低分辨率图像，我们使用 24x24 RGB 输入补丁。
def dataset_object(dataset_cache, training=True):
ds = dataset_cache
ds = ds.map(
lambda lowres, highres: random_crop(lowres, highres, scale=4),
num_parallel_calls=AUTOTUNE,
)
if training:
ds = ds.map(random_rotate, num_parallel_calls=AUTOTUNE)
ds = ds.map(flip_left_right, num_parallel_calls=AUTOTUNE)
# Batching Data
ds = ds.batch(16)
if training:
# Repeating Data, so that cardinality if dataset becomes infinte
ds = ds.repeat()
# prefetching allows later images to be prepared while the current image is being processed
ds = ds.prefetch(buffer_size=AUTOTUNE)
return ds
train_ds = dataset_object(train_cache, training=True)
val_ds = dataset_object(val_cache, training=False)
可视化数据
让我们可视化一些示例图像
lowres, highres = next(iter(train_ds))
# High Resolution Images
plt.figure(figsize=(10, 10))
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(highres[i].numpy().astype("uint8"))
plt.title(highres[i].shape)
plt.axis("off")
# Low Resolution Images
plt.figure(figsize=(10, 10))
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(lowres[i].numpy().astype("uint8"))
plt.title(lowres[i].shape)
plt.axis("off")
def PSNR(super_resolution, high_resolution):
"""Compute the peak signal-to-noise ratio, measures quality of image."""
# Max value of pixel is 255
psnr_value = tf.image.psnr(high_resolution, super_resolution, max_val=255)[0]
return psnr_value
构建模型
在论文中，作者训练了三个模型：EDSR、MDSR 和一个基线模型。在此代码示例中，我们只训练基线模型。
与具有三个残差块的模型比较
EDSR 的残差块设计与 ResNet 的不同。批归一化层已被移除（以及最终的 ReLU 激活）：由于批归一化层对特征进行归一化，因此它们会损害输出值范围的灵活性。因此最好将其移除。此外，它还有助于减少模型所需的 GPU RAM 量，因为批归一化层消耗的内存量与前面的卷积层相同。
class EDSRModel(tf.keras.Model):
def train_step(self, data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
# Update metrics (includes the metric that tracks the loss)
self.compiled_metrics.update_state(y, y_pred)
# Return a dict mapping metric names to current value
return {m.name: m.result() for m in self.metrics}
def predict_step(self, x):
# Adding dummy dimension using tf.expand_dims and converting to float32 using tf.cast
x = tf.cast(tf.expand_dims(x, axis=0), tf.float32)
# Passing low resolution image to model
super_resolution_img = self(x, training=False)
# Clips the tensor from min(0) to max(255)
super_resolution_img = tf.clip_by_value(super_resolution_img, 0, 255)
# Rounds the values of a tensor to the nearest integer
super_resolution_img = tf.round(super_resolution_img)
# Removes dimensions of size 1 from the shape of a tensor and converting to uint8
super_resolution_img = tf.squeeze(
tf.cast(super_resolution_img, tf.uint8), axis=0
)
return super_resolution_img
# Residual Block
def ResBlock(inputs):
x = layers.Conv2D(64, 3, padding="same", activation="relu")(inputs)
x = layers.Conv2D(64, 3, padding="same")(x)
x = layers.Add()([inputs, x])
return x
# Upsampling Block
def Upsampling(inputs, factor=2, **kwargs):
x = layers.Conv2D(64 * (factor ** 2), 3, padding="same", **kwargs)(inputs)
x = tf.nn.depth_to_space(x, block_size=factor)
x = layers.Conv2D(64 * (factor ** 2), 3, padding="same", **kwargs)(x)
x = tf.nn.depth_to_space(x, block_size=factor)
return x
def make_model(num_filters, num_of_residual_blocks):
# Flexible Inputs to input_layer
input_layer = layers.Input(shape=(None, None, 3))
# Scaling Pixel Values
x = layers.Rescaling(scale=1.0 / 255)(input_layer)
x = x_new = layers.Conv2D(num_filters, 3, padding="same")(x)
# 16 residual blocks
for _ in range(num_of_residual_blocks):
x_new = ResBlock(x_new)
x_new = layers.Conv2D(num_filters, 3, padding="same")(x_new)
x = layers.Add()([x, x_new])
x = Upsampling(x)
x = layers.Conv2D(3, 3, padding="same")(x)
output_layer = layers.Rescaling(scale=255)(x)
return EDSRModel(input_layer, output_layer)
model = make_model(num_filters=64, num_of_residual_blocks=16)
训练模型
# Using adam optimizer with initial learning rate as 1e-4, changing learning rate after 5000 steps to 5e-5
optim_edsr = keras.optimizers.Adam(
learning_rate=keras.optimizers.schedules.PiecewiseConstantDecay(
boundaries=[5000], values=[1e-4, 5e-5]
)
)
# Compiling model with loss as mean absolute error(L1 Loss) and metric as psnr
model.compile(optimizer=optim_edsr, loss="mae", metrics=[PSNR])
# Training for more epochs will improve results
model.fit(train_ds, epochs=100, steps_per_epoch=200, validation_data=val_ds)
Epoch 1/100
200/200 [==============================] - 78s 322ms/step - loss: 27.7075 - PSNR: 19.4656 - val_loss: 14.7192 - val_PSNR: 22.5129
Epoch 2/100
200/200 [==============================] - 6s 28ms/step - loss: 12.6842 - PSNR: 24.7269 - val_loss: 12.4348 - val_PSNR: 22.8793
Epoch 3/100
200/200 [==============================] - 6s 28ms/step - loss: 10.7646 - PSNR: 27.3775 - val_loss: 10.6830 - val_PSNR: 24.9075
Epoch 4/100
200/200 [==============================] - 6s 28ms/step - loss: 9.8356 - PSNR: 27.4924 - val_loss: 9.2714 - val_PSNR: 27.7680
Epoch 5/100
200/200 [==============================] - 5s 27ms/step - loss: 9.1752 - PSNR: 29.4013 - val_loss: 8.7747 - val_PSNR: 27.6017
Epoch 6/100
200/200 [==============================] - 6s 30ms/step - loss: 8.8630 - PSNR: 27.8686 - val_loss: 8.7710 - val_PSNR: 30.2381
Epoch 7/100
200/200 [==============================] - 5s 27ms/step - loss: 8.7107 - PSNR: 28.4887 - val_loss: 8.3186 - val_PSNR: 29.4744
Epoch 8/100
200/200 [==============================] - 6s 32ms/step - loss: 8.5374 - PSNR: 28.9546 - val_loss: 8.4716 - val_PSNR: 28.8873
Epoch 9/100
200/200 [==============================] - 5s 27ms/step - loss: 8.4111 - PSNR: 30.2234 - val_loss: 8.1969 - val_PSNR: 28.9538
Epoch 10/100
200/200 [==============================] - 6s 28ms/step - loss: 8.3835 - PSNR: 29.7066 - val_loss: 8.9434 - val_PSNR: 31.9213
Epoch 11/100
200/200 [==============================] - 5s 27ms/step - loss: 8.1713 - PSNR: 30.7191 - val_loss: 8.2816 - val_PSNR: 30.7049
Epoch 12/100
200/200 [==============================] - 6s 30ms/step - loss: 7.9129 - PSNR: 30.3964 - val_loss: 8.9365 - val_PSNR: 26.2667
Epoch 13/100
200/200 [==============================] - 5s 27ms/step - loss: 8.2504 - PSNR: 30.1612 - val_loss: 7.8384 - val_PSNR: 28.4159
Epoch 14/100
200/200 [==============================] - 6s 31ms/step - loss: 8.0114 - PSNR: 30.2370 - val_loss: 7.2658 - val_PSNR: 29.4454
Epoch 15/100
200/200 [==============================] - 5s 27ms/step - loss: 8.0059 - PSNR: 30.7665 - val_loss: 7.6692 - val_PSNR: 31.8294
Epoch 16/100
200/200 [==============================] - 6s 28ms/step - loss: 7.9388 - PSNR: 30.5297 - val_loss: 7.7625 - val_PSNR: 28.6685
Epoch 17/100
200/200 [==============================] - 5s 27ms/step - loss: 7.8627 - PSNR: 30.8213 - val_loss: 8.1984 - val_PSNR: 30.9864
Epoch 18/100
200/200 [==============================] - 6s 30ms/step - loss: 7.8956 - PSNR: 30.4661 - val_loss: 8.2664 - val_PSNR: 34.1168
Epoch 19/100
200/200 [==============================] - 5s 27ms/step - loss: 7.7800 - PSNR: 30.3071 - val_loss: 7.9547 - val_PSNR: 30.9254
Epoch 20/100
200/200 [==============================] - 6s 31ms/step - loss: 7.7402 - PSNR: 30.7251 - val_loss: 7.9632 - val_PSNR: 31.7438
Epoch 21/100
200/200 [==============================] - 5s 27ms/step - loss: 7.7372 - PSNR: 31.3348 - val_loss: 8.0512 - val_PSNR: 29.4988
Epoch 22/100
200/200 [==============================] - 6s 28ms/step - loss: 7.7207 - PSNR: 31.1984 - val_loss: 7.6072 - val_PSNR: 32.6720
Epoch 23/100
200/200 [==============================] - 6s 29ms/step - loss: 7.5955 - PSNR: 31.3128 - val_loss: 6.8593 - val_PSNR: 28.1123
Epoch 24/100
200/200 [==============================] - 6s 28ms/step - loss: 7.6341 - PSNR: 31.6670 - val_loss: 7.4485 - val_PSNR: 30.0567
Epoch 25/100
200/200 [==============================] - 6s 28ms/step - loss: 7.5404 - PSNR: 31.5332 - val_loss: 6.8795 - val_PSNR: 33.6179
Epoch 26/100
200/200 [==============================] - 6s 31ms/step - loss: 7.4429 - PSNR: 32.3681 - val_loss: 7.5937 - val_PSNR: 32.5076
Epoch 27/100
200/200 [==============================] - 6s 28ms/step - loss: 7.4243 - PSNR: 31.2899 - val_loss: 7.0982 - val_PSNR: 37.4561
Epoch 28/100
200/200 [==============================] - 5s 27ms/step - loss: 7.3542 - PSNR: 31.3620 - val_loss: 7.5735 - val_PSNR: 29.3892
Epoch 29/100
200/200 [==============================] - 6s 31ms/step - loss: 7.2648 - PSNR: 32.0806 - val_loss: 7.7589 - val_PSNR: 28.5829
Epoch 30/100
200/200 [==============================] - 5s 27ms/step - loss: 7.2954 - PSNR: 32.3495 - val_loss: 7.1625 - val_PSNR: 32.0560
Epoch 31/100
200/200 [==============================] - 6s 31ms/step - loss: 7.4815 - PSNR: 32.3662 - val_loss: 7.8601 - val_PSNR: 35.0962
Epoch 32/100
200/200 [==============================] - 6s 29ms/step - loss: 7.3957 - PSNR: 30.4455 - val_loss: 7.4800 - val_PSNR: 31.9397
Epoch 33/100
200/200 [==============================] - 6s 29ms/step - loss: 7.3849 - PSNR: 32.0058 - val_loss: 7.2225 - val_PSNR: 35.5276
Epoch 34/100
200/200 [==============================] - 6s 28ms/step - loss: 7.4285 - PSNR: 31.6806 - val_loss: 7.3937 - val_PSNR: 30.4433
Epoch 35/100
200/200 [==============================] - 6s 30ms/step - loss: 7.3841 - PSNR: 32.1425 - val_loss: 7.6458 - val_PSNR: 30.7912
Epoch 36/100
200/200 [==============================] - 5s 27ms/step - loss: 7.3049 - PSNR: 31.7272 - val_loss: 7.5190 - val_PSNR: 33.2980
Epoch 37/100
200/200 [==============================] - 6s 31ms/step - loss: 7.3098 - PSNR: 31.7727 - val_loss: 8.0041 - val_PSNR: 26.8507
Epoch 38/100
200/200 [==============================] - 6s 28ms/step - loss: 7.4027 - PSNR: 31.1814 - val_loss: 7.7334 - val_PSNR: 29.2905
Epoch 39/100
200/200 [==============================] - 6s 29ms/step - loss: 7.2470 - PSNR: 31.3636 - val_loss: 7.1275 - val_PSNR: 33.1772
Epoch 40/100
200/200 [==============================] - 6s 28ms/step - loss: 7.1907 - PSNR: 32.7381 - val_loss: 7.3437 - val_PSNR: 33.7216
Epoch 41/100
200/200 [==============================] - 6s 29ms/step - loss: 7.3383 - PSNR: 31.6409 - val_loss: 6.8769 - val_PSNR: 29.9654
Epoch 42/100
200/200 [==============================] - 5s 27ms/step - loss: 7.3393 - PSNR: 31.4941 - val_loss: 6.1088 - val_PSNR: 35.7083
Epoch 43/100
200/200 [==============================] - 6s 32ms/step - loss: 7.2272 - PSNR: 32.2356 - val_loss: 7.4534 - val_PSNR: 29.5734
Epoch 44/100
200/200 [==============================] - 6s 30ms/step - loss: 7.1773 - PSNR: 32.0016 - val_loss: 7.4676 - val_PSNR: 33.0795
Epoch 45/100
200/200 [==============================] - 6s 28ms/step - loss: 7.4677 - PSNR: 32.3508 - val_loss: 7.2459 - val_PSNR: 31.6806
Epoch 46/100
200/200 [==============================] - 6s 30ms/step - loss: 7.2347 - PSNR: 33.3392 - val_loss: 7.0098 - val_PSNR: 27.1658
Epoch 47/100
200/200 [==============================] - 6s 28ms/step - loss: 7.4494 - PSNR: 32.1602 - val_loss: 8.0211 - val_PSNR: 29.9740
Epoch 48/100
200/200 [==============================] - 6s 28ms/step - loss: 7.1128 - PSNR: 32.1696 - val_loss: 7.0101 - val_PSNR: 32.8874
Epoch 49/100
200/200 [==============================] - 6s 31ms/step - loss: 7.1698 - PSNR: 32.0733 - val_loss: 7.5813 - val_PSNR: 26.1697
Epoch 50/100
200/200 [==============================] - 6s 30ms/step - loss: 7.1904 - PSNR: 31.9198 - val_loss: 6.3655 - val_PSNR: 33.4935
Epoch 51/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0957 - PSNR: 32.3727 - val_loss: 7.2626 - val_PSNR: 28.8388
Epoch 52/100
200/200 [==============================] - 6s 30ms/step - loss: 7.1436 - PSNR: 32.2141 - val_loss: 7.6012 - val_PSNR: 31.2261
Epoch 53/100
200/200 [==============================] - 6s 28ms/step - loss: 7.2270 - PSNR: 32.2675 - val_loss: 6.9826 - val_PSNR: 27.6408
Epoch 54/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0638 - PSNR: 32.5191 - val_loss: 6.6046 - val_PSNR: 32.3862
Epoch 55/100
200/200 [==============================] - 6s 31ms/step - loss: 7.1609 - PSNR: 31.6787 - val_loss: 7.3563 - val_PSNR: 28.3834
Epoch 56/100
200/200 [==============================] - 6s 30ms/step - loss: 7.1953 - PSNR: 31.9948 - val_loss: 6.5111 - val_PSNR: 34.0409
Epoch 57/100
200/200 [==============================] - 6s 30ms/step - loss: 7.1168 - PSNR: 32.3288 - val_loss: 6.7979 - val_PSNR: 31.8126
Epoch 58/100
200/200 [==============================] - 6s 29ms/step - loss: 7.0578 - PSNR: 33.1605 - val_loss: 6.8349 - val_PSNR: 32.0840
Epoch 59/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0890 - PSNR: 32.7020 - val_loss: 7.4109 - val_PSNR: 31.8377
Epoch 60/100
200/200 [==============================] - 6s 29ms/step - loss: 7.1357 - PSNR: 32.9600 - val_loss: 7.7647 - val_PSNR: 30.2965
Epoch 61/100
200/200 [==============================] - 6s 32ms/step - loss: 7.2003 - PSNR: 32.0152 - val_loss: 7.8508 - val_PSNR: 27.8501
Epoch 62/100
200/200 [==============================] - 6s 30ms/step - loss: 7.0474 - PSNR: 32.4485 - val_loss: 7.3319 - val_PSNR: 28.4571
Epoch 63/100
200/200 [==============================] - 6s 30ms/step - loss: 7.1315 - PSNR: 32.6996 - val_loss: 7.0695 - val_PSNR: 34.9915
Epoch 64/100
200/200 [==============================] - 6s 28ms/step - loss: 7.1181 - PSNR: 32.9488 - val_loss: 6.2144 - val_PSNR: 33.9663
Epoch 65/100
200/200 [==============================] - 6s 29ms/step - loss: 7.1262 - PSNR: 32.0699 - val_loss: 7.1910 - val_PSNR: 34.1321
Epoch 66/100
200/200 [==============================] - 6s 28ms/step - loss: 7.2891 - PSNR: 32.5745 - val_loss: 6.9004 - val_PSNR: 34.5732
Epoch 67/100
200/200 [==============================] - 6s 31ms/step - loss: 6.8185 - PSNR: 32.2085 - val_loss: 6.8353 - val_PSNR: 27.2619
Epoch 68/100
200/200 [==============================] - 7s 33ms/step - loss: 6.9238 - PSNR: 33.3510 - val_loss: 7.3350 - val_PSNR: 28.2281
Epoch 69/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0037 - PSNR: 31.6955 - val_loss: 6.5887 - val_PSNR: 30.3138
Epoch 70/100
200/200 [==============================] - 5s 27ms/step - loss: 7.0239 - PSNR: 32.6923 - val_loss: 6.6467 - val_PSNR: 36.0194
Epoch 71/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0828 - PSNR: 32.0297 - val_loss: 6.5626 - val_PSNR: 34.4241
Epoch 72/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0717 - PSNR: 32.5201 - val_loss: 7.5056 - val_PSNR: 31.4176
Epoch 73/100
200/200 [==============================] - 6s 29ms/step - loss: 7.0943 - PSNR: 32.4469 - val_loss: 7.0981 - val_PSNR: 33.2052
Epoch 74/100
200/200 [==============================] - 8s 38ms/step - loss: 7.0288 - PSNR: 32.2301 - val_loss: 6.9661 - val_PSNR: 34.0108
Epoch 75/100
200/200 [==============================] - 6s 28ms/step - loss: 7.1122 - PSNR: 32.1658 - val_loss: 6.9569 - val_PSNR: 30.8972
Epoch 76/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0108 - PSNR: 31.5408 - val_loss: 7.1185 - val_PSNR: 26.8445
Epoch 77/100
200/200 [==============================] - 6s 28ms/step - loss: 6.7812 - PSNR: 32.4927 - val_loss: 7.0030 - val_PSNR: 31.6901
Epoch 78/100
200/200 [==============================] - 6s 29ms/step - loss: 6.9885 - PSNR: 31.9727 - val_loss: 7.1126 - val_PSNR: 29.0163
Epoch 79/100
200/200 [==============================] - 6s 30ms/step - loss: 7.0738 - PSNR: 32.4997 - val_loss: 6.7849 - val_PSNR: 31.0740
Epoch 80/100
200/200 [==============================] - 6s 29ms/step - loss: 7.0899 - PSNR: 31.7940 - val_loss: 6.9975 - val_PSNR: 33.6309
Epoch 81/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0215 - PSNR: 32.6563 - val_loss: 6.5724 - val_PSNR: 35.1765
Epoch 82/100
200/200 [==============================] - 6s 28ms/step - loss: 6.9076 - PSNR: 32.9912 - val_loss: 6.8611 - val_PSNR: 31.8409
Epoch 83/100
200/200 [==============================] - 6s 28ms/step - loss: 6.9978 - PSNR: 32.7159 - val_loss: 6.4787 - val_PSNR: 31.5799
Epoch 84/100
200/200 [==============================] - 6s 29ms/step - loss: 7.1276 - PSNR: 32.8232 - val_loss: 7.9006 - val_PSNR: 27.5171
Epoch 85/100
200/200 [==============================] - 7s 33ms/step - loss: 7.0276 - PSNR: 32.3290 - val_loss: 8.5374 - val_PSNR: 25.2824
Epoch 86/100
200/200 [==============================] - 7s 33ms/step - loss: 7.0434 - PSNR: 31.4983 - val_loss: 6.9392 - val_PSNR: 35.9229
Epoch 87/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0703 - PSNR: 32.2641 - val_loss: 7.8662 - val_PSNR: 28.1676
Epoch 88/100
200/200 [==============================] - 6s 28ms/step - loss: 7.1311 - PSNR: 32.2141 - val_loss: 7.2089 - val_PSNR: 27.3218
Epoch 89/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0730 - PSNR: 33.3360 - val_loss: 6.7915 - val_PSNR: 29.1367
Epoch 90/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0177 - PSNR: 32.6117 - val_loss: 8.3779 - val_PSNR: 31.9831
Epoch 91/100
200/200 [==============================] - 6s 31ms/step - loss: 6.9638 - PSNR: 32.2765 - val_loss: 6.6582 - val_PSNR: 37.5391
Epoch 92/100
200/200 [==============================] - 6s 28ms/step - loss: 6.9623 - PSNR: 32.8864 - val_loss: 7.7435 - val_PSNR: 29.8939
Epoch 93/100
200/200 [==============================] - 6s 29ms/step - loss: 6.8474 - PSNR: 32.5345 - val_loss: 6.8181 - val_PSNR: 28.1166
Epoch 94/100
200/200 [==============================] - 6s 28ms/step - loss: 6.9059 - PSNR: 32.0613 - val_loss: 7.0014 - val_PSNR: 33.2055
Epoch 95/100
200/200 [==============================] - 6s 29ms/step - loss: 7.0418 - PSNR: 32.2906 - val_loss: 6.9686 - val_PSNR: 28.8045
Epoch 96/100
200/200 [==============================] - 6s 30ms/step - loss: 6.8624 - PSNR: 32.5043 - val_loss: 7.2015 - val_PSNR: 33.2103
Epoch 97/100
200/200 [==============================] - 7s 33ms/step - loss: 6.9632 - PSNR: 33.0834 - val_loss: 7.0972 - val_PSNR: 30.3407
Epoch 98/100
200/200 [==============================] - 6s 31ms/step - loss: 6.9307 - PSNR: 31.9062 - val_loss: 7.3421 - val_PSNR: 31.5380
Epoch 99/100
200/200 [==============================] - 6s 28ms/step - loss: 7.0685 - PSNR: 31.9839 - val_loss: 7.9828 - val_PSNR: 33.0619
Epoch 100/100
200/200 [==============================] - 6s 28ms/step - loss: 6.9233 - PSNR: 31.8346 - val_loss: 6.3802 - val_PSNR: 38.4415
<keras.callbacks.History at 0x7fe3682f2c90>
对新图像运行推理并绘制结果
def plot_results(lowres, preds):
"""
Displays low resolution image and super resolution image
"""
plt.figure(figsize=(24, 14))
plt.subplot(132), plt.imshow(lowres), plt.title("Low resolution")
plt.subplot(133), plt.imshow(preds), plt.title("Prediction")
plt.show()
for lowres, highres in val.take(10):
lowres = tf.image.random_crop(lowres, (150, 150, 3))
preds = model.predict_step(lowres)
plot_results(lowres, preds)
最后说明
在本示例中，我们实现了 EDSR 模型（用于单图像超分辨率的增强深度残差网络）。您可以通过将模型训练更多轮次来提高模型精度，以及使用具有混合降级因子的更广泛的输入范围进行训练，以便能够处理更广泛的真实世界图像。
您还可以通过实现 EDSR+ 或 MDSR（多尺度超分辨率）和 MDSR+ 来改进给定的基线 EDSR 模型，这些模型是在同一篇论文中提出的。
| 训练后的模型 | 演示 |
|---|---|
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