MaxSR: Image Super-Resolution Using Improved MaxViT
Bincheng Yang Gangshan Wu
While transformer models have been demonstrated to be effective for natural
language processing tasks and high-level vision tasks, only a few attempts have
been made to use powerful transformer models for single image super-resolution.
Because transformer models have powerful representation capacity and the
in-built self-attention mechanisms in transformer models help to leverage
self-similarity prior in input low-resolution image to improve performance for
single image super-resolution, we present a single image super-resolution model
based on recent hybrid vision transformer of MaxViT, named as MaxSR. MaxSR
consists of four parts, a shallow feature extraction block, multiple cascaded
adaptive MaxViT blocks to extract deep hierarchical features and model global
self-similarity from low-level features efficiently, a hierarchical feature
fusion block, and finally a reconstruction block. The key component of MaxSR,
i.e., adaptive MaxViT block, is based on MaxViT block which mixes MBConv with
squeeze-and-excitation, block attention and grid attention. In order to achieve
better global modelling of self-similarity in input low-resolution image, we
improve block attention and grid attention in MaxViT block to adaptive block
attention and adaptive grid attention which do self-attention inside each
window across all grids and each grid across all windows respectively in the
most efficient way. We instantiate proposed model for classical single image
super-resolution (MaxSR) and lightweight single image super-resolution
(MaxSR-light). Experiments show that our MaxSR and MaxSR-light establish new
state-of-the-art performance efficiently.