  Deep unfolding networks (DUNs) are the foremost methods in the realm of
compressed sensing MRI, as they can employ learnable networks to facilitate
interpretable forward-inference operators. However, several daunting issues
still exist, including the heavy dependency on the first-order optimization
algorithms, the insufficient information fusion mechanisms, and the limitation
of capturing long-range relationships. To address the issues, we propose a
Generically Accelerated Half-Quadratic Splitting (GA-HQS) algorithm that
incorporates second-order gradient information and pyramid attention modules
for the delicate fusion of inputs at the pixel level. Moreover, a multi-scale
split transformer is also designed to enhance the global feature
representation. Comprehensive experiments demonstrate that our method surpasses
previous ones on single-coil MRI acceleration tasks.
