Papers › Pre-Trained Image Processing Transformer
Pre-Trained Image Processing Transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao
As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, 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. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. 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. Code is available at https://github.com/huawei-noah/Pretrained-IPT and https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Color Image Denoising | CBSD68 sigma50 | IPT | PSNR | 29.39 | #1 of 18 | Archive leaderboard | report |
| Color Image Denoising | Urban100 sigma50 | IPT | PSNR | 29.71 | #8 of 9 | Archive leaderboard | report |
| Image Super-Resolution | BSD100 - 2x upscaling | IPT | PSNR | 32.48 | #13 of 30 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 3x upscaling | IPT | PSNR | 30.85 | #9 of 24 | Archive leaderboard | report |
| Image Super-Resolution | Urban100 - 3x upscaling | IPT | PSNR | 29.49 | #9 of 22 | Archive leaderboard | report |
| Single Image Deraining | Rain100L | IPT | PSNR | 41.62 | #1 of 19 | Archive leaderboard | report |
| Single Image Deraining | Rain100L | IPT | SSIM | 0.988 | #1 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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