  Classical methods of medical image super-resolution (MISR) utilize open-loop
architecture with implicit under-resolution (UR) unit and explicit
super-resolution (SR) unit. The UR unit can always be given, assumed, or
estimated, while the SR unit is elaborately designed according to various SR
algorithms. The closed-loop feedback mechanism is widely employed in current
MISR approaches and can efficiently improve their performance. The feedback
mechanism may be divided into two categories: local feedback and global
feedback. Therefore, this paper proposes a global feedback-based closed-cycle
framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements.
Mathematical model and closed-loop equation of CMISR are built. Mathematical
proof with Taylor-series approximation indicates that CMISR has zero recovery
error in steady-state. In addition, CMISR holds plug-and-play characteristic
that fuses model-based and learning-based approaches and can be established on
any existing MISR algorithms. Five CMISR algorithms are respectively proposed
based on the state-of-the-art open-loop MISR algorithms. Experimental results
with three scale factors and on three open medical image datasets show that
CMISR is superior to MISR in reconstruction performance and is particularly
suited to medical images with strong edges or intense contrast.
