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
a simple, efficient, and intuitive text editor

Oxygen beta a simple, efficient, and intuitive text editor Overview oxygen is a simple, efficient, and intuitive text editor designed as more featured

Aarush Gupta 1 Feb 23, 2022
This repository contains the code used for the implementation of the paper "Probabilistic Regression with HuberDistributions"

Public_prob_regression_with_huber_distributions This repository contains the code used for the implementation of the paper "Probabilistic Regression w

David Mohlin 1 Dec 04, 2021
The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Targeted Neural Dynamical Modeling Note: This is a re-implementation (in Tensorflow2) of the original TNDM model. We do not plan to further update the

6 Oct 05, 2022
YOLOv5 Series Multi-backbone, Pruning and quantization Compression Tool Box.

YOLOv5-Compression Update News Requirements 环境安装 pip install -r requirements.txt Evaluation metric Visdrone Model mAP ZhangYuan 719 Jan 02, 2023

A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

Torch-RecHub A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend. 安装 pip install torch-rechub 主要特性 scikit-learn风格易用

Mincai Lai 67 Jan 04, 2023
L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalabilty

Kim, Taehoon 102 Dec 21, 2022
A machine learning project which can detect and predict the skin disease through image recognition.

ML-Project-2021 A machine learning project which can detect and predict the skin disease through image recognition. The dataset used for this is the H

Debshishu Ghosh 1 Jan 13, 2022
Framework to build and train RL algorithms

RayLink RayLink is a RL framework used to build and train RL algorithms. RayLink was used to build a RL framework, and tested in a large-scale multi-a

Bytedance Inc. 32 Oct 07, 2022
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

McMahon Lab 230 Jan 05, 2023
Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation (CVPR2022) https://arxiv.org/abs/2203.08483 Unpaired image-to-image (I2I

Xueqi Hu 50 Dec 16, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3：CCKS2021中文NLP地址相关性任务

基于 bert4keras 的一个baseline 不作任何 数据trick 单模 线上 最高可到 0.7891 # 基础 版 train.py 0.7769 # transformer 各层 cls concat 明神的trick https://xv44586.git

孙永松 7 Dec 28, 2021
4K videos with annotated masks in our ICCV2021 paper 'Internal Video Inpainting by Implicit Long-range Propagation'.

Annotated 4K Videos paper | project website | code | demo video 4K videos with annotated object masks in our ICCV2021 paper: Internal Video Inpainting

Tengfei Wang 21 Nov 05, 2022
A python implementation of Deep-Image-Analogy based on pytorch.

Deep-Image-Analogy This project is a python implementation of Deep Image Analogy.https://arxiv.org/abs/1705.01088. Some results Requirements python 3

Peng Lu 171 Dec 14, 2022
Convenient tool for speeding up the intern/officer review process.

icpc-app-screen Convenient tool for speeding up the intern/officer applicant review process. Eliminates the pain from reading application responses of

1 Oct 30, 2021
A Python framework for conversational search

Chatty Goose Multi-stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting Installation Ma

Castorini 36 Oct 23, 2022
Project for music generation system based on object tracking and CGAN

Project for music generation system based on object tracking and CGAN The project was inspired by MIDINet: A Convolutional Generative Adversarial Netw

1 Nov 21, 2021
PyTorch implementation of adversarial patch

adversarial-patch PyTorch implementation of adversarial patch This is an implementation of the Adversarial Patch paper. Not official and likely to hav

Jamie Hayes 172 Nov 29, 2022
StarGAN2 for practice

StarGAN2 for practice This version of StarGAN2 (coined as 'Post-modern Style Transfer') is intended mostly for fellow artists, who rarely look at scie

vadim epstein 87 Sep 24, 2022
Exploring whether attention is necessary for vision transformers

Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet Paper/Report TL;DR We replace the attention layer in a v

Luke Melas-Kyriazi 461 Jan 07, 2023
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Daniel Bourke 3.4k Jan 07, 2023