  Transformer-based methods have exhibited remarkable potential in single image
super-resolution (SISR) by effectively extracting long-range dependencies.
However, most of the current research in this area has prioritized the design
of transformer blocks to capture global information, while overlooking the
importance of incorporating high-frequency priors, which we believe could be
beneficial. In our study, we conducted a series of experiments and found that
transformer structures are more adept at capturing low-frequency information,
but have limited capacity in constructing high-frequency representations when
compared to their convolutional counterparts. Our proposed solution, the
cross-refinement adaptive feature modulation transformer (CRAFT), integrates
the strengths of both convolutional and transformer structures. It comprises
three key components: the high-frequency enhancement residual block (HFERB) for
extracting high-frequency information, the shift rectangle window attention
block (SRWAB) for capturing global information, and the hybrid fusion block
(HFB) for refining the global representation. To tackle the inherent
intricacies of transformer structures, we introduce a frequency-guided
post-training quantization (PTQ) method aimed at enhancing CRAFT's efficiency.
These strategies incorporate adaptive dual clipping and boundary refinement. To
further amplify the versatility of our proposed approach, we extend our PTQ
strategy to function as a general quantization method for transformer-based
SISR techniques. Our experimental findings showcase CRAFT's superiority over
current state-of-the-art methods, both in full-precision and quantization
scenarios. These results underscore the efficacy and universality of our PTQ
strategy.
