This paper introduces a groundbreaking multi-modal neural network model
designed for resolution enhancement, which innovatively leverages
inter-diagnostic correlations within a system. Traditional approaches have
primarily focused on uni-modal enhancement strategies, such as pixel-based
image enhancement or heuristic signal interpolation. In contrast, our model
employs a novel methodology by harnessing the diagnostic relationships within
the physics of fusion plasma. Initially, we establish the correlation among
diagnostics within the tokamak. Subsequently, we utilize these correlations to
substantially enhance the temporal resolution of the Thomson Scattering
diagnostic, which assesses plasma density and temperature. By increasing its
resolution from conventional 200Hz to 500kHz, we facilitate a new level of
insight into plasma behavior, previously attainable only through
computationally intensive simulations. This enhancement goes beyond simple
interpolation, offering novel perspectives on the underlying physical phenomena
governing plasma dynamics.

本文介绍了一种突破性的多模态神经网络模型，旨在提高分辨率，创新性地利用系统内的诊断相关性。与传统方法主要侧重于单模态增强策略（如基于像素的图像增强或启发式信号插值）不同，我们的模型通过利用聚变等离子体中的诊断关系，采用一种新颖的方法。首先，我们建立了托卡马克内诊断仪之间的相关性。随后，我们利用这些相关性，大幅提高汤姆逊散射诊断的时间分辨率，该诊断评估等离子体密度和温度。通过将其分辨率从传统的 200Hz 增加到 500kHz，我们为研究等离子体行为提供了新的洞察层面，之前只能通过计算密集型模拟才能达到。这种增强超越了简单的插值，提供了对控制等离子体动力学的基础物理现象的新视角。

Diag2Diag: 物理发现的多模态超分辨率，应用于核聚变

Diag2Diag: Multi modal super resolution for physics discovery with  application to fusion

Light field (LF) imaging captures both angular and spatial light
distributions, enabling advanced photographic techniques. However, micro-lens
array (MLA)- based cameras face a spatial-angular resolution tradeoff due to a
single shared sensor. We propose a novel light field framework for resolution
enhancement, employing a modular approach. The first module generates a
high-resolution, all-in-focus image. The second module, a texture transformer
network, enhances the resolution of each light field perspective independently
using the output of the first module as a reference image. The final module
leverages light field regularity to jointly improve resolution across all LF
image perspectives. Our approach demonstrates superior performance to existing
methods in both qualitative and quantitative evaluations.

光场成像具有捕捉角度和空间光分布的能力，通过模块化方法进行分辨率增强，以每个光场视角独立增强分辨率的纹理变换网络，并利用光场的规则性来同时提高所有光场图像视角的分辨率，相比现有方法在定性和定量评估方面表现出更好的性能。

光场空间分辨率增强框架

Light Field Spatial Resolution Enhancement Framework

Computed tomography (CT) is a widely used non-invasive medical imaging
technique for disease diagnosis. The diagnostic accuracy is often affected by
image resolution, which can be insufficient in practice. For medical CT images,
the through-plane resolution is often worse than the in-plane resolution and
there can be overlap between slices, causing difficulties in diagnoses.
Self-supervised methods for through-plane resolution enhancement, which train
on in-plane images and infer on through-plane images, have shown promise for
both CT and MRI imaging. However, existing self-supervised methods either
neglect overlap or can only handle specific cases with fixed combinations of
resolution and overlap. To address these limitations, we propose a
self-supervised method called SR4ZCT. It employs the same off-axis training
approach while being capable of handling arbitrary combinations of resolution
and overlap. Our method explicitly models the relationship between resolutions
and voxel spacings of different planes to accurately simulate training images
that match the original through-plane images. We highlight the significance of
accurate modeling in self-supervised off-axis training and demonstrate the
effectiveness of SR4ZCT using a real-world dataset.

我们提出了一种自我监督方法 SR4ZCT，通过模拟匹配原始切片图像的训练图像，准确建模了分辨率和不同切片之间的像素间距的关系，以处理任意分辨率和重叠组合的医学 CT 图像，提高了诊断准确度。