  Transformer-based models have revolutionized the field of image
super-resolution (SR) by harnessing their inherent ability to capture complex
contextual features. The overlapping rectangular shifted window technique used
in transformer architecture nowadays is a common practice in super-resolution
models to improve the quality and robustness of image upscaling. However, it
suffers from distortion at the boundaries and has limited unique shifting
modes. To overcome these weaknesses, we propose a non-overlapping triangular
window technique that synchronously works with the rectangular one to mitigate
boundary-level distortion and allows the model to access more unique sifting
modes. In this paper, we propose a Composite Fusion Attention Transformer
(CFAT) that incorporates triangular-rectangular window-based local attention
with a channel-based global attention technique in image super-resolution. As a
result, CFAT enables attention mechanisms to be activated on more image pixels
and captures long-range, multi-scale features to improve SR performance. The
extensive experimental results and ablation study demonstrate the effectiveness
of CFAT in the SR domain. Our proposed model shows a significant 0.7 dB
performance improvement over other state-of-the-art SR architectures.
