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IPT_denoise30.pt | 2 years ago | |
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License.txt | 2 years ago | |
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dataloader.py | 2 years ago | |
inference.py | 2 years ago | |
main.py | 2 years ago | |
option.py | 2 years ago | |
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trainer.py | 2 years ago | |
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By Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao. [arXiv]
We study the low-level computer vision task (such as denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). We present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks.
The benchmark datasets can be downloaded as follows:
For super-resolution:
Set5,
Set14,
B100,
Urban100.
Please refer to EDSR-PyTorch
For denoising:
CBSD68,
Urban100.
For deraining:
Rain100.
Please refer to PReNet
The result images are converted into YCbCr color space. The PSNR is evaluated on the Y channel only.
This is the inference script of IPT, you can following steps to finish the test of image processing tasks, like SR, denoise and derain, via the corresponding pretrained models.
For details about hyperparameters, see option.py.
The pretrained models are available in google drive
Inference example:
For SR x2,x3,x4:
python main.py --dir_data $DATA_PATH --pretrain $MODEL_PATH --data_test Set5+Set14+B100+Urban100 --scale $SCALE
For Denoise 30,50:
python main.py --dir_data $DATA_PATH --pretrain $MODEL_PATH --data_test CBSD68+Urban100 --scale 1 --denoise --sigma $NOISY_LEVEL
For derain:
python main.py --dir_data $DATA_PATH --pretrain $MODEL_PATH --scale 1 --derain
Method | Scale | Set5 | Set14 | B100 | Urban100 |
---|---|---|---|---|---|
VDSR | X2 | 37.53 | 33.05 | 31.90 | 30.77 |
EDSR | X2 | 38.11 | 33.92 | 32.32 | 32.93 |
RCAN | X2 | 38.27 | 34.12 | 32.41 | 33.34 |
RDN | X2 | 38.24 | 34.01 | 32.34 | 32.89 |
OISR-RK3 | X2 | 38.21 | 33.94 | 32.36 | 33.03 |
RNAN | X2 | 38.17 | 33.87 | 32.32 | 32.73 |
SAN | X2 | 38.31 | 34.07 | 32.42 | 33.1 |
HAN | X2 | 38.27 | 34.16 | 32.41 | 33.35 |
IGNN | X2 | 38.24 | 34.07 | 32.41 | 33.23 |
IPT (ours) | X2 | 38.37 | 34.43 | 32.48 | 33.76 |
Method | Scale | Set5 | Set14 | B100 | Urban100 |
---|---|---|---|---|---|
VDSR | X3 | 33.67 | 29.78 | 28.83 | 27.14 |
EDSR | X3 | 34.65 | 30.52 | 29.25 | 28.80 |
RCAN | X3 | 34.74 | 30.65 | 29.32 | 29.09 |
RDN | X3 | 34.71 | 30.57 | 29.26 | 28.80 |
OISR-RK3 | X3 | 34.72 | 30.57 | 29.29 | 28.95 |
RNAN | X3 | 34.66 | 30.52 | 29.26 | 28.75 |
SAN | X3 | 34.75 | 30.59 | 29.33 | 28.93 |
HAN | X3 | 34.75 | 30.67 | 29.32 | 29.10 |
IGNN | X3 | 34.72 | 30.66 | 29.31 | 29.03 |
IPT (ours) | X3 | 34.81 | 30.85 | 29.38 | 29.49 |
Method | Scale | Set5 | Set14 | B100 | Urban100 |
---|---|---|---|---|---|
VDSR | X4 | 31.35 | 28.02 | 27.29 | 25.18 |
EDSR | X4 | 32.46 | 28.80 | 27.71 | 26.64 |
RCAN | X4 | 32.63 | 28.87 | 27.77 | 26.82 |
SAN | X4 | 32.64 | 28.92 | 27.78 | 26.79 |
RDN | X4 | 32.47 | 28.81 | 27.72 | 26.61 |
OISR-RK3 | X4 | 32.53 | 28.86 | 27.75 | 26.79 |
RNAN | X4 | 32.49 | 28.83 | 27.72 | 26.61 |
HAN | X4 | 32.64 | 28.90 | 27.80 | 26.85 |
IGNN | X4 | 32.57 | 28.85 | 27.77 | 26.84 |
IPT (ours) | X4 | 32.64 | 29.01 | 27.82 | 27.26 |
@misc{chen2020pre,
title={Pre-Trained Image Processing Transformer},
author={Chen, Hanting and Wang, Yunhe and Guo, Tianyu and Xu, Chang and Deng, Yiping and Liu, Zhenhua and Ma, Siwei and Xu, Chunjing and Xu, Chao and Gao, Wen},
year={2021},
eprint={2012.00364},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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