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。

CMISR：循环医学图像超分辨率

CMISR: Circular Medical Image Super-Resolution

Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying
anatomical structures and aiding in accurate diagnosis. Medical image
super-resolution (SR) reconstruction using deep learning techniques can enhance
lesion analysis and assist doctors in improving diagnostic efficiency and
accuracy. However, existing deep learning-based SR methods predominantly rely
on convolutional neural networks (CNNs), which inherently limit the expressive
capabilities of these models and therefore make it challenging to discover
potential relationships between different image features. To overcome this
limitation, we propose an A-network that utilizes multiple convolution operator
feature extraction modules (MCO) for extracting image features using multiple
convolution operators. These extracted features are passed through multiple
sets of cross-feature extraction modules (MSC) to highlight key features
through inter-channel feature interactions, enabling subsequent feature
learning. An attention-based sparse graph neural network module is incorporated
to establish relationships between pixel features, learning which adjacent
pixels have the greatest impact on determining the features to be filled. To
evaluate our model's effectiveness, we conducted experiments using different
models on data generated from multiple datasets with different degradation
multiples, and the experimental results show that our method is a significant
improvement over the current state-of-the-art methods.

提出利用多个卷积算子特征提取模块的 A 网络，通过多次跨特征提取模块的交互来突出关键特征，并使用基于注意力的稀疏图神经网络模块建立像素特征之间的关系，以弥补卷积神经网络模型表达能力有限的缺陷，在医学图像分辨率提高方面获得了显著的改进。