Official implementation of the paper 'Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution'

Related tags

Deep LearningDASR
Overview

DASR

Paper

Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
Jie Liang, Hui Zeng, and Lei Zhang.
In arxiv preprint.

Abstract

Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task due to the unknown complex degradation of real-world images and the limited computation resources in practical applications. Recent research on Real-ISR has achieved significant progress by modeling the image degradation space; however, these methods largely rely on heavy backbone networks and they are inflexible to handle images of different degradation levels. In this paper, we propose an efficient and effective degradation-adaptive super-resolution (DASR) network, whose parameters are adaptively specified by estimating the degradation of each input image. Specifically, a tiny regression network is employed to predict the degradation parameters of the input image, while several convolutional experts with the same topology are jointly optimized to specify the network parameters via a non-linear mixture of experts. The joint optimization of multiple experts and the degradation-adaptive pipeline significantly extend the model capacity to handle degradations of various levels, while the inference remains efficient since only one adaptively specified network is used for super-resolving the input image. Our extensive experiments demonstrate that the proposed DASR is not only much more effective than existing methods on handling real-world images with different degradation levels but also efficient for easy deployment.

Overall pipeline of the DASR:

illustration

For more details, please refer to our paper.

Getting started

  • Clone this repo.
git clone https://github.com/csjliang/DASR
cd DASR
  • 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/DASR/train_DASR.yml, then

  • Single GPU:
PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0 python dasr/train.py -opt options/train/DASR/train_DASR.yml --auto_resume
  • Distributed Training:
YTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4335 dasr/train.py -opt options/train/DASR/train_DASR.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/DASR/test_DASR.yml, then run:

PYTHONPATH="./:${PYTHONPATH}" CUDA_VISIBLE_DEVICES=0 python basicsr/test.py -opt options/test/DASR/test_DASR.yml

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

License

This project is released under the Apache 2.0 license.

Citation

@article{jie2022DASR,
  title={Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution},
  author={Liang, Jie and Zeng, Hui and Zhang, Lei},
  journal={arXiv preprint arXiv:2203.14216},
  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].

Learning Super-Features for Image Retrieval

Learning Super-Features for Image Retrieval This repository contains the code for running our FIRe model presented in our ICLR'22 paper: @inproceeding

NAVER 101 Dec 28, 2022
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

AntonMu 616 Jan 08, 2023
Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker A example FastAPI PyTorch Model deploy with nvidia/cuda base docker. Model

Ming 68 Jan 04, 2023
Flybirds - BDD-driven natural language automated testing framework, present by Trip Flight

Flybird | English Version 行为驱动开发(Behavior-driven development,缩写BDD),是一种软件过程的思想或者

Ctrip, Inc. 706 Dec 30, 2022
Text Extraction Formulation + Feedback Loop for state-of-the-art WSD (EMNLP 2021)

ConSeC is a novel approach to Word Sense Disambiguation (WSD), accepted at EMNLP 2021. It frames WSD as a text extraction task and features a feedback loop strategy that allows the disambiguation of

Sapienza NLP group 36 Dec 13, 2022
Evaluation and Benchmarking of Speech Super-resolution Methods

Speech Super-resolution Evaluation and Benchmarking What this repo do: A toolbox for the evaluation of speech super-resolution algorithms. Unify the e

Haohe Liu (刘濠赫) 84 Dec 20, 2022
This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".

Deep Conditional Gaussian Mixture Model for Constrained Clustering. This repository holds the code for the paper Deep Conditional Gaussian Mixture Mod

17 Oct 30, 2022
Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data

1 Meta-FDMIxup Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021) paper News! the rep

Fu Yuqian 44 Nov 18, 2022
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving This is the source code for our paper Frequency Domain Image Tran

Mu Cai 52 Dec 23, 2022
A set of tools for converting a darknet dataset to COCO format working with YOLOX

darknet格式数据→COCO darknet训练数据目录结构(详情参见dataset/darknet): darknet ├── class.names ├── gen_config.data ├── gen_train.txt ├── gen_valid.txt └── images

RapidAI-NG 148 Jan 03, 2023
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks, ICLR 2021 (Spotlight) Demo | Paper [NEW!] Time to play with our interac

Shengyu Zhao 373 Jan 02, 2023
CCP dataset from Clothing Co-Parsing by Joint Image Segmentation and Labeling

Clothing Co-Parsing (CCP) Dataset Clothing Co-Parsing (CCP) dataset is a new clothing database including elaborately annotated clothing items. 2, 098

Wei Yang 434 Dec 24, 2022
[NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators"

G-PATE This is the official code base for our NeurIPS 2021 paper: "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of T

AI Secure 14 Oct 12, 2022
GPU-Accelerated Deep Learning Library in Python

Hebel GPU-Accelerated Deep Learning Library in Python Hebel is a library for deep learning with neural networks in Python using GPU acceleration with

Hannes Bretschneider 1.2k Dec 21, 2022
This is the official implementation code repository of Underwater Light Field Retention : Neural Rendering for Underwater Imaging (Accepted by CVPR Workshop2022 NTIRE)

Underwater Light Field Retention : Neural Rendering for Underwater Imaging (UWNR) (Accepted by CVPR Workshop2022 NTIRE) Authors: Tian Ye†, Sixiang Che

jmucsx 17 Dec 14, 2022
[ICML 2022] The official implementation of Graph Stochastic Attention (GSAT).

Graph Stochastic Attention (GSAT) The official implementation of GSAT for our paper: Interpretable and Generalizable Graph Learning via Stochastic Att

85 Nov 27, 2022
Ranger - a synergistic optimizer using RAdam (Rectified Adam), Gradient Centralization and LookAhead in one codebase

Ranger-Deep-Learning-Optimizer Ranger - a synergistic optimizer combining RAdam (Rectified Adam) and LookAhead, and now GC (gradient centralization) i

Less Wright 1.1k Dec 21, 2022
[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”,

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in

VITA 24 Dec 17, 2022
Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Adaptive Segmentation Mask Attack This repository contains the implementation of the Adaptive Segmentation Mask Attack (ASMA), a targeted adversarial

Utku Ozbulak 53 Jul 04, 2022