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
Automatic Number Plate Recognition using Contours and Convolution Neural Networks (CNN)

Cite our paper if you find this project useful https://www.ijariit.com/manuscripts/v7i4/V7I4-1139.pdf Abstract Image processing technology is used in

Adithya M 2 Jun 28, 2022
Christmas face app for Decathlon xmas coding party!

Christmas Face Application Use this library to create the perfect picture for your christmas cards! Done by Hasib Zunair, Guillaume Brassard and Samue

Hasib Zunair 4 Dec 20, 2021
Simulating Sycamore quantum circuits classically using tensor network algorithm.

Simulating the Sycamore quantum supremacy circuit This repo contains data we have obtained in simulating the Sycamore quantum supremacy circuits with

Feng Pan 46 Nov 17, 2022
Code for database and frontend of webpage for Neural Fields in Visual Computing and Beyond.

Neural Fields in Visual Computing—Complementary Webpage This is based on the amazing MiniConf project from Hendrik Strobelt and Sasha Rush—thank you!

Brown University Visual Computing Group 29 Nov 30, 2022
Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service

Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a

Tianxiang Sun 149 Jan 04, 2023
Scaling and Benchmarking Self-Supervised Visual Representation Learning

FAIR Self-Supervision Benchmark is deprecated. Please see VISSL, a ground-up rewrite of benchmark in PyTorch. FAIR Self-Supervision Benchmark This cod

Meta Research 584 Dec 31, 2022
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022
Transparent Transformer Segmentation

Transparent Transformer Segmentation Introduction This repository contains the data and code for IJCAI 2021 paper Segmenting transparent object in the

谢恩泽 140 Jan 02, 2023
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

78 Dec 27, 2022
Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

CSRL Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning Python: 3

4 Apr 14, 2022
Autolfads-tf2 - A TensorFlow 2.0 implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS

autolfads-tf2 A TensorFlow 2.0 implementation of LFADS and AutoLFADS. Installati

Systems Neural Engineering Lab 11 Oct 29, 2022
This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

AtlasNet V2 - Learning Elementary Structures This work was build upon Thibault Groueix's AtlasNet and 3D-CODED projects. (you might want to have a loo

Théo Deprelle 123 Nov 11, 2022
Explore extreme compression for pre-trained language models

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

twinkle 16 Nov 14, 2022
Causal Imitative Model for Autonomous Driving

Causal Imitative Model for Autonomous Driving Mohammad Reza Samsami, Mohammadhossein Bahari, Saber Salehkaleybar, Alexandre Alahi. arXiv 2021. [Projec

VITA lab at EPFL 8 Oct 04, 2022
A texturizer that I just made. Nothing special here.

texturizer This is a little project that I did with an hour's time. It texturizes an image given a image and a texture to texturize it with. There is

1 Nov 11, 2021
RLHive: a framework designed to facilitate research in reinforcement learning.

RLHive is a framework designed to facilitate research in reinforcement learning. It provides the components necessary to run a full RL experiment, for both single agent and multi agent environments.

88 Jan 05, 2023
Attentive Implicit Representation Networks (AIR-Nets)

Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

29 Dec 07, 2022
"Learning Free Gait Transition for Quadruped Robots vis Phase-Guided Controller"

PhaseGuidedControl The current version is developed based on the old version of RaiSim series, and possibly requires further modification. It will be

X-Mechanics 12 Oct 21, 2022
A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.

Automatic_Background_Remover A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku. 👉 https:

Gaurav 16 Oct 29, 2022
PyTorch Implementation of AnimeGANv2

PyTorch implementation of AnimeGANv2

4k Jan 07, 2023