  Recent advances have demonstrated the powerful capability of transformer
architecture in image restoration. However, our analysis indicates that
existing transformerbased methods can not establish both exact global and local
dependencies simultaneously, which are much critical to restore the details and
missing content of degraded images. To this end, we present an efficient image
processing transformer architecture with hierarchical attentions, called IPTV2,
adopting a focal context self-attention (FCSA) and a global grid self-attention
(GGSA) to obtain adequate token interactions in local and global receptive
fields. Specifically, FCSA applies the shifted window mechanism into the
channel self-attention, helps capture the local context and mutual interaction
across channels. And GGSA constructs long-range dependencies in the
cross-window grid, aggregates global information in spatial dimension.
Moreover, we introduce structural re-parameterization technique to feed-forward
network to further improve the model capability. Extensive experiments
demonstrate that our proposed IPT-V2 achieves state-of-the-art results on
various image processing tasks, covering denoising, deblurring, deraining and
obtains much better trade-off for performance and computational complexity than
previous methods. Besides, we extend our method to image generation as latent
diffusion backbone, and significantly outperforms DiTs.
