  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
