  This paper proposes UHDformer, a general Transformer for
Ultra-High-Definition (UHD) image restoration. UHDformer contains two learning
spaces: (a) learning in high-resolution space and (b) learning in
low-resolution space. The former learns multi-level high-resolution features
and fuses low-high features and reconstructs the residual images, while the
latter explores more representative features learning from the high-resolution
ones to facilitate better restoration. To better improve feature representation
in low-resolution space, we propose to build feature transformation from the
high-resolution space to the low-resolution one. To that end, we propose two
new modules: Dual-path Correlation Matching Transformation module (DualCMT) and
Adaptive Channel Modulator (ACM). The DualCMT selects top C/r (r is greater or
equal to 1 which controls the squeezing level) correlation channels from the
max-pooling/mean-pooling high-resolution features to replace low-resolution
ones in Transformers, which can effectively squeeze useless content to improve
the feature representation in low-resolution space to facilitate better
recovery. The ACM is exploited to adaptively modulate multi-level
high-resolution features, enabling to provide more useful features to
low-resolution space for better learning. Experimental results show that our
UHDformer reduces about ninety-seven percent model sizes compared with most
state-of-the-art methods while significantly improving performance under
different training sets on 3 UHD image restoration tasks, including low-light
image enhancement, image dehazing, and image deblurring. The source codes will
be made available at https://github.com/supersupercong/UHDformer.
