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 and global feedback.
Therefore, this paper proposes a global feedback-based closed-cycle framework,
circular MISR (CMISR), with unambiguous UR and 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 which 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.

该研究提出了一种基于全局反馈的闭环超分辨率医学图像增强框架（circular MISR），在稳态下具有零恢复误差，并且在重建性能方面优于传统方法。