Giter Site home page Giter Site logo

pesr's Introduction

PESR

Official implementation for Perception-Enhanced Single Image Super-Resolution via Relativistic Generative Networks ECCV Workshop 2018

Citation

Please our project if it is helpful for your research

@InProceedings{Vu_2018_ECCV_Workshops},
author = {Vu, Thang and Luu, Tung M. and Yoo, Chang D.},
title = {Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks},
booktitle = {The European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}

PSNR vs PESR

Dependencies

  • Nvidia GPUs (training takes 1 day on 4 Titan Xp GPUs)
  • At least 16G RAM
  • Python3
  • Pytorch 0.4
  • tensorboardX
  • tqdm
  • imageio

Datasets, models, and results

Dataset

  • Train: DIV2K (800 2K-resolution images)
  • Valid (for visualization): DIV2K (100 val images), PIRM (100 self-val images)
  • Test: Set5, Set14, B100, Urban100, PIRM (100 self-val images), DIV2K (100 val images)
  • Download train+val+test datasets
  • Download test only dataset

Pretrained models

  • Download pretrained models including 1 PSNR-optimized model and 1 perception-optimized model

Paper results

Quick start

  • Download test dataset and put into data/origin/ directory
  • Download pretrained model and put into check_point directory
  • Run python test.py --dataset <DATASET_NAME>
  • Results will be saved into results/ directory

Training

  • Download train+val+test dataset and put into data/origin directory
  • Pretrain with L1 loss: python train.py --phase pretrain --learning_rate 1e-4
  • Finetune on pretrained model with GAN: python train.py
  • Models with be saved into check_point/ direcory

Visualization

  • Start tensorboard: tensorboard --logdir check_point
  • Enter: YOUR_IP:6006 to your web browser.
  • Tensorboard when finetuning on pretrained model should be similar to:

Tensorboard

Tensorboard_imgs

Comprehensive testing

  • Test perceptual model: follow Quick start
  • Interpolate between perceptual model and PSNR model: python test.py --dataset <DATASET> --alpha <ALPHA> (with alpha being perceptual weight)
  • Test perceptual quality: refer to PIRM validation code

Quantitative and Qualitative results

RED and BLUE indicate best and second best respectively.

References

pesr's People

Contributors

thangvubk avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.