HINet: Half Instance Normalization Network for Image Restoration

Related tags

Deep LearningHINet
Overview

PWC PWC PWC PWC PWC PWC PWC

HINet: Half Instance Normalization Network for Image Restoration

Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, Chengpeng Chen

Paper: https://arxiv.org/abs/2105.06086

In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8 times and 2.9 times speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3 times speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4 times speedup. With HINet, we won 1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG Artifacts, with a PSNR of 29.70.

Network Architecture

arch

Installation

This implementation based on BasicSR which is a open source toolbox for image/video restoration tasks.

python 3.6.9
pytorch 1.5.1
cuda 10.1
git clone https://github.com/megvii-model/HINet
cd HINet
pip install -r requirements.txt
python setup.py develop --no_cuda_ext

Image Restoration Tasks

Image denoise, deblur, derain.

Image Denoise - SIDD dataset (Coming soon)
Image Deblur - GoPro dataset (Click to expand)

  • prepare data

    • mkdir ./datasets/GoPro

    • download the train set in ./datasets/GoPro/train and test set in ./datasets/GoPro/test (refer to MPRNet)

    • it should be like:

      ./datasets/
./datasets/GoPro/
./datasets/GoPro/train/
./datasets/GoPro/train/input/
./datasets/GoPro/train/target/
./datasets/GoPro/test/
./datasets/GoPro/test/input/
./datasets/GoPro/test/target/

    • python scripts/data_preparation/gopro.py

      • crop the train image pairs to 512x512 patches.

  • eval

    • download pretrained model to ./experiments/pretrained_models/HINet-GoPro.pth
    • python basicsr/test.py -opt options/test/REDS/HINet-GoPro.yml

  • train

    • python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/GoPro/HINet.yml --launcher pytorch
Image Deblur - REDS dataset (Click to expand)

  • prepare data

    • mkdir ./datasets/REDS

    • download the train / val set from train_blur, train_sharp, val_blur, val_sharp to ./datasets/REDS/ and unzip them.

    • it should be like

      ./datasets/
./datasets/REDS/
./datasets/REDS/val/
./datasets/REDS/val/val_blur_jpeg/
./datasets/REDS/val/val_sharp/
./datasets/REDS/train/
./datasets/REDS/train/train_blur_jpeg/
./datasets/REDS/train/train_sharp/

    • python scripts/data_preparation/reds.py

      • flatten the folders and extract 300 validation images.

  • eval

    • download pretrained model to ./experiments/pretrained_models/HINet-REDS.pth
    • python basicsr/test.py -opt options/test/REDS/HINet-REDS.yml

  • train

    • python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/REDS/HINet.yml --launcher pytorch
Image Derain - Rain13k dataset (Click to expand)

  • prepare data

    • mkdir ./datasets/Rain13k

    • download the train set and test set (refer to MPRNet)

    • it should be like

      ./datasets/
./datasets/Rain13k/
./datasets/Rain13k/train/
./datasets/Rain13k/train/input/
./datasets/Rain13k/train/target/
./datasets/Rain13k/test/
./datasets/Rain13k/test/Test100/
./datasets/Rain13k/test/Rain100H/
./datasets/Rain13k/test/Rain100L/
./datasets/Rain13k/test/Test2800/
./datasets/Rain13k/test/Test1200/

  • eval

    • download pretrained model to ./experiments/pretrained_models/HINet-Rain13k.pth

    • For Test100:

      • python basicsr/test.py -opt options/test/Rain13k/HINet-Test100.yml

    • For Rain100H

      • python basicsr/test.py -opt options/test/Rain13k/HINet-Rain100H.yml

    • For Rain100L

      • python basicsr/test.py -opt options/test/Rain13k/HINet-Rain100L.yml

    • For Test2800

      • python basicsr/test.py -opt options/test/Rain13k/HINet-Test2800.yml

    • For Test1200

      • python basicsr/test.py -opt options/test/Rain13k/HINet-Test1200.yml

  • train

    • python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train_rain.py -opt options/train/Rain13k/HINet.yml --launcher pytorch

Results

Some of the following results are higher than the original paper as we optimized some hyper-parameters.

NTIRE2021 Deblur Track2 ResultSIDD ResultGoPro Result
REDDS ResultRain13k Result

Citations

If HINet helps your research or work, please consider citing HINet.

@inproceedings{chen2021hinet,
  title={HINet: Half Instance Normalization Network for Image Restoration},
  author={Liangyu Chen and Xin Lu and Jie Zhang and Xiaojie Chu and Chengpeng Chen},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year={2021}
}

Contact

If you have any questions, please contact [email protected] or [email protected] .

Owner
GitHub Repository
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
Benchmark for Answering Existential First Order Queries with Single Free Variable

EFO-1-QA Benchmark for First Order Query Estimation on Knowledge Graphs This repository contains an entire pipeline for the EFO-1-QA benchmark. EFO-1

HKUST-KnowComp 14 Oct 24, 2022
FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data, a relatively complete set of integrated multi-source data download terminal software fast is developed. The softw

ChangChuntao 23 Dec 31, 2022
Job-Recommend-Competition - Vectorwise Interpretable Attentions for Multimodal Tabular Data

SiD - Simple Deep Model Vectorwise Interpretable Attentions for Multimodal Tabul

Jungwoo Park 40 Dec 22, 2022
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

Zhengxia Zou 56 Jul 15, 2022
This is a file about Unet implemented in Pytorch

Unet this is an implemetion of Unet in Pytorch and it's architecture is as follows which is the same with paper of Unet component of Unet Convolution

Dragon 1 Dec 03, 2021
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
A robust pointcloud registration pipeline based on correlation.

PHASER: A Robust and Correspondence-Free Global Pointcloud Registration Ubuntu 18.04+ROS Melodic: Overview Pointcloud registration using correspondenc

ETHZ ASL 101 Dec 01, 2022
This app is a simple example of using Strealit to create a financial data web app.

Streamlit Demo: Finance Chart This app is a simple example of using Streamlit to create a financial data web app. This demo use streamlit, pandas and

91 Jan 02, 2023
A module that used for encrypt code which includes RSA and AES

软件加密模块 requirement： Crypto,pycryptodome,pyqt5 本地加密信息为随机字符串 使用说明 命令行参数 -h 帮助 -checkWorking 检查是否能正常工作，后接1确认指令 -checkEndDate 检查截至日期，后接1确认指令 -activateCode

2 Sep 27, 2022
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place

Mikaela Uy 294 Dec 12, 2022
Corgis are the cutest creatures; have 30K of them!

corgi-net This is a dataset of corgi images scraped from the corgi subreddit. After filtering using an ImageNet classifier, the training set consists

Alex Nichol 6 Dec 24, 2022
COPA-SSE contains crowdsourced explanations for the Balanced COPA dataset

COPA-SSE Repository for COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning. COPA-SSE contains crowdsourced explanations for the Balanced

Ana Brassard 5 Jul 31, 2022
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

Introduction English | 简体中文 MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. The m

OpenMMLab 2.7k Jan 07, 2023
Human motion synthesis using Unity3D

Human motion synthesis using Unity3D Prerequisite: Software: amc2bvh.exe, Unity 2017, Blender. Unity: RockVR (Video Capture), scenes, character models

Hao Xu 9 Jun 01, 2022
PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules

Dynamic Routing Between Capsules - PyTorch implementation PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules from Sara Sabour,

Adam Bielski 475 Dec 24, 2022
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

yuanyao366 39 Dec 29, 2022
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

How to eat TensorFlow2 in 30 days ? 🔥 🔥 Click here for Chinese Version（中文版） 《10天吃掉那只pyspark》 🚀 github项目地址: https://github.com/lyhue1991/eat_pyspark

lyhue1991 9.7k Jan 01, 2023
[Link]mareteutral - pars tradg wth M []

pairs-trading-with-ML Jonathan Larkin, August 2017 One popular strategy classification is Pairs Trading. Though this category of strategies can exhibi

Jonathan Larkin 134 Jan 06, 2023
pybaum provides tools to work with pytrees which is a concept burrowed from JAX.

pybaum provides tools to work with pytrees which is a concept burrowed from JAX.

Open Source Economics 9 May 11, 2022