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.

翻译：摘要：经典医学图像超分辨率（MISR）方法采用开环架构，包含隐式欠分辨率（UR）单元与显式超分辨率（SR）单元。UR单元总是可给定、假设或估算的，而SR单元则根据不同的SR算法精细设计。当前MISR方法广泛采用闭环反馈机制，可有效提升性能。该反馈机制可分为两类：局部反馈与全局反馈。因此，本文提出一种基于全局反馈的闭环框架——循环式MISR（CMISR），包含明确的UR单元与先进的SR单元。构建了CMISR的数学模型与闭环方程。基于泰勒级数近似的数学证明表明，CMISR在稳态下具有零恢复误差。此外，CMISR具备即插即用特性，融合了基于模型与基于学习的方法，可建立在任何现有MISR算法之上。基于五种最先进的开环MISR算法，分别提出了相应的CMISR算法。在三组公开医学图像数据集上，针对三种尺度因子的实验结果表明，CMISR在重建性能上优于MISR，尤其适用于具有强边缘或强对比度的医学图像。