A TensorFlow-based image super-resolution model considering both quantitative and perceptual quality
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Four-pass perceptual super-resolution with enhanced upscaling


This repository contains a TensorFlow-based implementation of 4PP-EUSR ("Deep learning-based image super-resolution considering quantitative and perceptual quality"), which considers both the quantitative (e.g., PSNR) and perceptual quality (e.g., NIQE) of the upscaled images. Our method won the 2nd place for Region 2 in the PIRM Challenge on Perceptual Super Resolution at ECCV 2018.

BSD100 - 37073 ※ The perceptual index is calculated by "0.5 * ((10 - Ma) + NIQE)", which is used in the PIRM Challenge. Lower is better.

Followings are the performance comparison evaluated on the BSD100 dataset.

Method PSNR (dB) (↓) Perceptual Index
EDSR 27.796 5.326
MDSR 27.771 5.424
EUSR 27.674 5.307
SRResNet-MSE 27.601 5.217
4PP-EUSR (PIRM Challenge) 26.569 2.683
SRResNet-VGG22 26.322 5.183
SRGAN-MSE 25.981 2.802
Bicubic interpolation 25.957 6.995
SRGAN-VGG22 25.697 2.631
SRGAN-VGG54 25.176 2.351
CX 24.581 2.250

Please cite following papers when you use the code, pre-trained models, or results:


  • Python 3.6+
  • TensorFlow 1.8+

Test pre-trained models

Generating upscaled images from the trained models can be done by test/test.py. Following are the brief instructions.

  1. Download and copy the trained model available in Downloads section to the test/ folder.
  2. Place the low-resolution images (PNG only) to the test/LR/ folder.
  3. Run python test.py --model_name [model file name]. For example, if you downloaded the PIRM Challenge version of our pre-trained model, run python test.py --model_name 4pp_eusr_pirm.pb.
  4. The upscaled images will be available on the test/SR/ folder.

Please run python test.py --help for more information.

Training 4PP-EUSR

Currently, the training code is not available. We are working hard on writing the training code for public, so please stay tuned for further updates. :)


Pre-trained models:

Results (Set5, Set14, BSD100, PIRM):