Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

Related tags

Deep LearningLDL
Overview

LDL

Paper | Supplementary Material

Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
Jie Liang*, Hui Zeng*, and Lei Zhang.
In CVPR 2022 (Oral Presentation).

Abstract

Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts. Based on the observation that the local statistics (e.g., residual variance) of artifact areas are often different from the areas of perceptually friendly details, we develop a framework to discriminate between GAN-generated artifacts and realistic details, and consequently generate an artifact map to regularize and stabilize the model training process. Our proposed locally discriminative learning (LDL) method is simple yet effective, which can be easily plugged in off-the-shelf SISR methods and boost their performance. Experiments demonstrate that LDL outperforms the state-of-the-art GAN based SISR methods, achieving not only higher reconstruction accuracy but also superior perceptual quality on both synthetic and real-world datasets.

Overall illustration of the LDL:

illustration

For more details, please refer to our paper.

Getting started

  • Clone this repo.
git clone https://github.com/csjliang/LDL
cd LDL
  • Install dependencies. (Python 3 + NVIDIA GPU + CUDA. Recommend to use Anaconda)
pip install -r requirements.txt
  • Prepare the training and testing dataset by following this instruction.
  • Prepare the pre-trained models by following this instruction.

Training

First, check and adapt the yml file options/train/LDL/train_Synthetic_LDL.yml (or options/train/LDL/train_Realworld_LDL.yml for real-world image super-resolution), then

  • Single GPU:
PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/LDL/train_Synthetic_LDL.yml --auto_resume

or

PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0 python realesrgan/train.py -opt options/train/LDL/train_Realworld_LDL.yml --auto_resume
  • Distributed Training:
PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=5678 basicsr/train.py -opt options/train/LDL/train_Synthetic_LDL.yml --launcher pytorch --auto_resume

or

PYTHONPATH=":${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train/LDL/train_Realworld_LDL.yml --launcher pytorch --auto_resume

Training files (logs, models, training states and visualizations) will be saved in the directory ./experiments/{name}

Testing

First, check and adapt the yml file options/test/LDL/test_LDL_Synthetic_x4.yml (or options/test/LDL/test_LDL_Realworld_x4.yml for real-world image super-resolution), then

  • Calculate metrics and save visual results for synthetic tasks:
PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0 python basicsr/test.py -opt options/test/LDL/test_LDL_Synthetic_x4.yml
  • Save visual results for real-world image super-resolution:
PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0 python basicsr/test.py -opt options/test/LDL/test_LDL_Realworld_x4.yml

Evaluating files (logs and visualizations) will be saved in the directory ./results/{name}

The Training and testing steps for scale=2 are similar.

Get Quantitative Metrics

First, check and adapt the settings of the files in metrics, then (take PSNR as an example) run

PYTHONPATH="./:${PYTHONPATH}" python scripts/metrics/table_calculate_psnr_all.py

Other metrics are similar.

License

This project is released under the Apache 2.0 license.

Citation

@inproceedings{jie2022LDL,
  title={Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution},
  author={Liang, Jie and Zeng, Hui and Zhang, Lei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

Acknowledgement

This project is built based on the excellent BasicSR project.

Contact

Should you have any questions, please contact me via [email protected].

AirCode: A Robust Object Encoding Method

AirCode This repo contains source codes for the arXiv preprint "AirCode: A Robust Object Encoding Method" Demo Object matching comparison when the obj

Chen Wang 30 Dec 09, 2022
NeRF visualization library under construction

NeRF visualization library using PlenOctrees, under construction pip install nerfvis Docs will be at: https://nerfvis.readthedocs.org import nerfvis s

Alex Yu 196 Jan 04, 2023
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Octavio Arriaga 5.3k Dec 30, 2022
Reading list for research topics in Masked Image Modeling

awesome-MIM Reading list for research topics in Masked Image Modeling(MIM). We list the most popular methods for MIM, if I missed something, please su

ligang 231 Dec 07, 2022
Learnable Boundary Guided Adversarial Training (ICCV2021)

Learnable Boundary Guided Adversarial Training This repository contains the implementation code for the ICCV2021 paper: Learnable Boundary Guided Adve

DV Lab 27 Sep 25, 2022
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

Nikita 12 Dec 14, 2022
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation.

Understanding Minimum Bayes Risk Decoding This repo provides code and documentation for the following paper: Müller and Sennrich (2021): Understanding

ZurichNLP 13 May 01, 2022
Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

Updated Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol. Introduction This balenaCloud (previously

Remko 1 Oct 17, 2021
the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

BGNet This repository contains the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet] Environment Python 3.6.* C

3DCV developer 87 Nov 29, 2022
Github for the conference paper GLOD-Gaussian Likelihood OOD detector

FOOD - Fast OOD Detector Pytorch implamentation of the confernce peper FOOD arxiv link. Abstract Deep neural networks (DNNs) perform well at classifyi

17 Jun 19, 2022
Simulation of self-focusing of laser beams in condensed media

What is it? Program for scientific research, which allows to simulate the phenomenon of self-focusing of different laser beams (including Gaussian, ri

Evgeny Vasilyev 13 Dec 24, 2022
Automatic Video Captioning Evaluation Metric --- EMScore

Automatic Video Captioning Evaluation Metric --- EMScore Overview For an illustration, EMScore can be computed as: Installation modify the encode_text

Yaya Shi 17 Nov 28, 2022
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images This repository contains the implementation of our paper MetaAvatar: Learni

sfwang 96 Dec 13, 2022
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

SSAN Introduction This is the pytorch implementation of the SSAN model (see our AAAI2021 paper: Entity Structure Within and Throughout: Modeling Menti

benfeng 69 Nov 15, 2022
[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

CC 4.4k Dec 27, 2022
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images Histological Image Segmentation This

Saad Wazir 11 Dec 16, 2022
RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow. They have a parallel sampling feature in order to increase computation speed (especially in high-performance computing (HPC)).

Fangjian Li 3 Dec 28, 2021
Preparation material for Dropbox interviews

Dropbox-Onsite-Interviews A guide for the Dropbox onsite interview! The Dropbox interview question bank is very small. The bank has been in a Chinese

386 Dec 31, 2022
Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks.

Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks. Generally, we intergrete different kind of functional

28 Jan 08, 2023
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Dec 31, 2022