The official pytorch implementation of the paper Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods. In this paper, we propose a simple baseline that exceeds the SOTA methods and is computationally efficient. To further simplify the baseline, we reveal that the nonlinear activation functions, e.g. Sigmoid, ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by multiplication or removed. Thus, we derive a Nonlinear Activation Free Network, namely NAFNet, from the baseline. SOTA results are achieved on various challenging benchmarks, e.g. 33.69 dB PSNR on GoPro (for image deblurring), exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs; 40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28 dB with less than half of its computational costs.
NAFNet based Stereo Image Super-Resolution solution won the 1st place on the NTIRE 2022 Stereo Image Super-resolution Challenge! Coming Soon.
This implementation based on BasicSR which is a open source toolbox for image/video restoration tasks and HINet
python 3.9.5
pytorch 1.11.0
cuda 11.3git clone https://github.com/megvii-research/NAFNet
cd NAFNet
pip install -r requirements.txt
python setup.py develop --no_cuda_ext
Image Denoise - SIDD dataset (Click to expand)
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prepare data
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mkdir ./datasets/SIDD -
download the SIDD-Medium sRGB Dataset in here and unzip it. Move Data (./SIDD_Medium_Srgb/Data) set to ./datasets/SIDD/ or make a soft link. Download val files (ValidationNoisyBlocksSrgb.mat and ValidationGtBlocksSrgb.mat) in ./datasets/SIDD/ .
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it should be like:
./datasets/SIDD/Data ./datasets/SIDD/ValidationNoisyBlocksSrgb.mat ./datasets/SIDD/ValidationGtBlocksSrgb.mat
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python scripts/data_preparation/sidd.py- crop the train image pairs to 512x512 patches
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eval
- download pretrained model to ./experiments/pretrained_models/NAFNet-SIDD-width64.pth
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/SIDD/NAFNet-width64.yml --launcher pytorch- distributed evaluation. Set nproc_per_node to 1 for single gpu evaluation.
calc_psnr(pred, gt)rather thancalc_psnr(pred.round(), gt)to avoid the PSNR loss caused by the "round()" operation, following HINet, MPRNet, and etc.
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train
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/SIDD/NAFNet-width64.yml --launcher pytorch
Image Deblur - GoPro dataset (Click to expand)
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prepare data
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mkdir ./datasets/GoPro -
download the train set to ./datasets/GoPro/train and test set to ./datasets/GoPro/test (refer to MPRNet)
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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/
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python scripts/data_preparation/gopro.py- crop the train image pairs to 512x512 patches.
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eval
- download pretrained model to ./experiments/pretrained_models/NAFNet-GoPro-width64.pth
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/GoPro/NAFNet-width64.yml --launcher pytorch- distributed evaluation. Set nproc_per_node to 1 for single gpu evaluation.
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train
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/GoPro/NAFNet-width64.yml --launcher pytorch
If NAFNet helps your research or work, please consider citing NAFNet.
@article{chen2022simple,
title={Simple Baselines for Image Restoration},
author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},
journal={arXiv preprint arXiv:2204.04676},
year={2022}
}
If you have any questions, please contact [email protected] or [email protected]
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