Pre-Trained Image Processing Transformer (IPT)

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.

MindSpore Code

• Official MindSpore code
• Pretrained weights for super-resolution X4

Requirements

• python 3
• pytorch == 1.4.0
• torchvision

Dataset

The benchmark datasets can be downloaded as follows:

For super-resolution:

Set5, Set14, B100, Urban100.

For denoising:

CBSD68, Urban100.

For deraining:

Rain100L.

The result images are converted into YCbCr color space. The PSNR is evaluated on the Y channel only.

Script Description

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.

Script Parameter

For details about hyperparameters, see option.py.

Evaluation

Pretrained models

The pretrained models are available in google drive

Evaluation Process

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

Results

• Detailed results on image super-resolution task.

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

• Super-resolution result

• Denoising result

• Derain result

Citation

@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}
}

Acknowledgement

• Main code from EDSR-PyTorch
• Transformer code from detr