  Recently, window-based attention methods have shown great potential for
computer vision tasks, particularly in Single Image Super-Resolution (SISR).
However, it may fall short in capturing long-range dependencies and
relationships between distant tokens. Additionally, we find that learning on
spatial domain does not convey the frequency content of the image, which is a
crucial aspect in SISR. To tackle these issues, we propose a new
Channel-Partitioned Attention Transformer (CPAT) to better capture long-range
dependencies by sequentially expanding windows along the height and width of
feature maps. In addition, we propose a novel Spatial-Frequency Interaction
Module (SFIM), which incorporates information from spatial and frequency
domains to provide a more comprehensive information from feature maps. This
includes information about the frequency content and enhances the receptive
field across the entire image. Experimental findings demonstrate the
effectiveness of our proposed modules and architecture. In particular, CPAT
surpasses current state-of-the-art methods by up to 0.31dB.
