  Transformers have shown significant effectiveness for various vision tasks
including both high-level vision and low-level vision. Recently, masked
autoencoders (MAE) for feature pre-training have further unleashed the
potential of Transformers, leading to state-of-the-art performances on various
high-level vision tasks. However, the significance of MAE pre-training on
low-level vision tasks has not been sufficiently explored. In this paper, we
show that masked autoencoders are also scalable self-supervised learners for
image processing tasks. We first present an efficient Transformer model
considering both channel attention and shifted-window-based self-attention
termed CSformer. Then we develop an effective MAE architecture for image
processing (MAEIP) tasks. Extensive experimental results show that with the
help of MAEIP pre-training, our proposed CSformer achieves state-of-the-art
performance on various image processing tasks, including Gaussian denoising,
real image denoising, single-image motion deblurring, defocus deblurring, and
image deraining.

Computer Vision and Pattern Recognition