|---|---|---|---|---| | [View on TensorFlow.org](https://www.tensorflow.org/hub/tutorials/image_enhancing) | [Run in Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/image_enhancing.ipynb) | [View on GitHub](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/image_enhancing.ipynb) | [Download notebook](https://storage.googleapis.com/tensorflow_docs/docs/site/en/hub/tutorials/image_enhancing.ipynb) | [See TF Hub model](https://tfhub.dev/captain-pool/esrgan-tf2/1) | This colab demonstrates use of TensorFlow Hub Module for Enhanced Super Resolution Generative Adversarial Network (*by Xintao Wang et.al.* ) \[[Paper](https://arxiv.org/pdf/1809.00219.pdf)\] \[[Code](https://github.com/captain-pool/GSOC/)\] 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" ``` /tmpfs/src/tf_docs_env/lib/python3.11/site-packages/tensorflow_hub/__init__.py:61: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. from pkg_resources import parse_version ``` wget "https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png" -O original.png ``` --2026-11-05 13:10:31-- https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png Resolving user-images.githubusercontent.com (user-images.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ... Connecting to user-images.githubusercontent.com (user-images.githubusercontent.com)|185.199.108.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 34146 (33K) [image/png] Saving to: ‘original.png’ original.png 0%[ ] 0 --.-KB/s original.png 100%[===================>] 33.35K --.-KB/s in 0.004s 2026-11-05 13:10:31 (8.83 MB/s) - ‘original.png’ saved [34146/34146] ``` # Declaring Constants IMAGE_PATH = "original.png" SAVED_MODEL_PATH = "https://tfhub.dev/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. """ 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) ``` WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1793884231.703220 74191 cuda_platform.cc:52] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected ``` # 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 ```  model = hub.load(SAVED_MODEL_PATH) ``` Downloading https://tfhub.dev/captain-pool/esrgan-tf2/1: 2.86MB Downloading https://tfhub.dev/captain-pool/esrgan-tf2/1: 12.86MB Downloading https://tfhub.dev/captain-pool/esrgan-tf2/1: 20.58MB Downloading https://tfhub.dev/captain-pool/esrgan-tf2/1: 20.60MB Downloaded https://tfhub.dev/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: 1.088763 ``` # 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 ```  ### Evaluating Performance of the Model !wget "https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64" -O test.jpg IMAGE_PATH = "test.jpg" ``` --2026-11-05 13:10:37-- https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64 Resolving lh4.googleusercontent.com (lh4.googleusercontent.com)... 173.194.206.132, 2607:f8b0:4001:c62::84 Connecting to lh4.googleusercontent.com (lh4.googleusercontent.com)|173.194.206.132|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 85465 (83K) [image/jpeg] Saving to: ‘test.jpg’ test.jpg 0%[ ] 0 --.-KB/s test.jpg 100%[===================>] 83.46K --.-KB/s in 0.001s 2026-11-05 13:10:38 (68.8 MB/s) - ‘test.jpg’ saved [85465/85465] ``` # 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")  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: 1.022331 ``` plot_image(tf.squeeze(fake_image), title="Super Resolution") # Calculating PSNR wrt Original Image psnr = tf.squeeze(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: 27.938789 ```  **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: 27.938789 ```