Image Super Resolution using ESRGAN

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This colab demonstrates use of TensorFlow Hub Module for Enhanced Super Resolution Generative Adversarial Network (by Xintao Wang et.al.) [Paper] [Code]

for image enhancing. (Preferrably bicubically downsampled images).

Model trained on DIV2K Dataset (on bicubically downsampled images) on image patches of size 128 x 128.

Preparing Environment

import os
import time
from PIL import Image
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"
wget "https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png" -O original.png
--2020-11-24 15:29:04--  https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png
Resolving user-images.githubusercontent.com (user-images.githubusercontent.com)... 151.101.192.133, 151.101.128.133, 151.101.64.133, ...
Connecting to user-images.githubusercontent.com (user-images.githubusercontent.com)|151.101.192.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 34146 (33K) [image/png]
Saving to: ‘original.png’

original.png        100%[===================>]  33.35K  --.-KB/s    in 0.003s  

2020-11-24 15:29:04 (9.56 MB/s) - ‘original.png’ saved [34146/34146]


# Declaring Constants
IMAGE_PATH = "original.png"
SAVED_MODEL_PATH = "https://hub.tensorflow.google.cn/captain-pool/esrgan-tf2/1"

Defining Helper Functions

def preprocess_image(image_path):
  """ Loads image from path and preprocesses to make it model ready
      Args:
        image_path: Path to the image file
  """
  hr_image = tf.image.decode_image(tf.io.read_file(image_path))
  # If PNG, remove the alpha channel. The model only supports
  # images with 3 color channels.
  if hr_image.shape[-1] == 4:
    hr_image = hr_image[...,:-1]
  hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
  hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
  hr_image = tf.cast(hr_image, tf.float32)
  return tf.expand_dims(hr_image, 0)

def save_image(image, filename):
  """
    Saves unscaled Tensor Images.
    Args:
      image: 3D image tensor. [height, width, channels]
      filename: Name of the file to save to.
  """
  if not isinstance(image, Image.Image):
    image = tf.clip_by_value(image, 0, 255)
    image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
  image.save("%s.jpg" % filename)
  print("Saved as %s.jpg" % filename)
%matplotlib inline
def plot_image(image, title=""):
  """
    Plots images from image tensors.
    Args:
      image: 3D image tensor. [height, width, channels].
      title: Title to display in the plot.
  """
  image = np.asarray(image)
  image = tf.clip_by_value(image, 0, 255)
  image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
  plt.imshow(image)
  plt.axis("off")
  plt.title(title)

Performing Super Resolution of images loaded from path

hr_image = preprocess_image(IMAGE_PATH)
# Plotting Original Resolution image
plot_image(tf.squeeze(hr_image), title="Original Image")
save_image(tf.squeeze(hr_image), filename="Original Image")
Saved as Original Image.jpg

png

model = hub.load(SAVED_MODEL_PATH)
Downloaded https://hub.tensorflow.google.cn/captain-pool/esrgan-tf2/1, Total size: 20.60MB


start = time.time()
fake_image = model(hr_image)
fake_image = tf.squeeze(fake_image)
print("Time Taken: %f" % (time.time() - start))
Time Taken: 2.803335

# Plotting Super Resolution Image
plot_image(tf.squeeze(fake_image), title="Super Resolution")
save_image(tf.squeeze(fake_image), filename="Super Resolution")
Saved as Super Resolution.jpg

png

Evaluating Performance of the Model

!wget "https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64" -O test.jpg
IMAGE_PATH = "test.jpg"
--2020-11-24 15:29:19--  https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64
Resolving lh4.googleusercontent.com (lh4.googleusercontent.com)... 108.177.125.132, 2404:6800:4008:c03::84
Connecting to lh4.googleusercontent.com (lh4.googleusercontent.com)|108.177.125.132|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84897 (83K) [image/jpeg]
Saving to: ‘test.jpg’

test.jpg            100%[===================>]  82.91K  --.-KB/s    in 0.001s  

2020-11-24 15:29:20 (75.5 MB/s) - ‘test.jpg’ saved [84897/84897]


# Defining helper functions
def downscale_image(image):
  """
      Scales down images using bicubic downsampling.
      Args:
          image: 3D or 4D tensor of preprocessed image
  """
  image_size = []
  if len(image.shape) == 3:
    image_size = [image.shape[1], image.shape[0]]
  else:
    raise ValueError("Dimension mismatch. Can work only on single image.")

  image = tf.squeeze(
      tf.cast(
          tf.clip_by_value(image, 0, 255), tf.uint8))

  lr_image = np.asarray(
    Image.fromarray(image.numpy())
    .resize([image_size[0] // 4, image_size[1] // 4],
              Image.BICUBIC))

  lr_image = tf.expand_dims(lr_image, 0)
  lr_image = tf.cast(lr_image, tf.float32)
  return lr_image
hr_image = preprocess_image(IMAGE_PATH)
lr_image = downscale_image(tf.squeeze(hr_image))
# Plotting Low Resolution Image
plot_image(tf.squeeze(lr_image), title="Low Resolution")

png

model = hub.load(SAVED_MODEL_PATH)
start = time.time()
fake_image = model(lr_image)
fake_image = tf.squeeze(fake_image)
print("Time Taken: %f" % (time.time() - start))
Time Taken: 1.224536

plot_image(tf.squeeze(fake_image), title="Super Resolution")
# Calculating PSNR wrt Original Image
psnr = tf.image.psnr(
    tf.clip_by_value(fake_image, 0, 255),
    tf.clip_by_value(hr_image, 0, 255), max_val=255)
print("PSNR Achieved: %f" % psnr)
PSNR Achieved: 28.029171

png

Comparing Outputs size by side.

plt.rcParams['figure.figsize'] = [15, 10]
fig, axes = plt.subplots(1, 3)
fig.tight_layout()
plt.subplot(131)
plot_image(tf.squeeze(hr_image), title="Original")
plt.subplot(132)
fig.tight_layout()
plot_image(tf.squeeze(lr_image), "x4 Bicubic")
plt.subplot(133)
fig.tight_layout()
plot_image(tf.squeeze(fake_image), "Super Resolution")
plt.savefig("ESRGAN_DIV2K.jpg", bbox_inches="tight")
print("PSNR: %f" % psnr)
PSNR: 28.029171

png