Motion artifact is a major challenge in magnetic resonance imaging (MRI) that
severely degrades image quality, reduces examination efficiency, and makes
accurate diagnosis difficult. However, previous methods often relied on
implicit models for artifact correction, resulting in biases in modeling the
artifact formation mechanism and characterizing the relationship between
artifact information and anatomical details. These limitations have hindered
the ability to obtain high-quality MR images. In this work, we incorporate the
artifact generation mechanism to reestablish the relationship between artifacts
and anatomical content in the image domain, highlighting the superiority of
explicit models over implicit models in medical problems. Based on this, we
propose a novel end-to-end image domain model called AF2R, which addresses this
problem using conditional normalization flow. Specifically, we first design a
feature encoder to extract anatomical features from images with motion
artifacts. Then, through a series of reversible transformations using the
feature-to-image flow module, we progressively obtain MR images unaffected by
motion artifacts. Experimental results on simulated and real datasets
demonstrate that our method achieves better performance in both quantitative
and qualitative results, preserving better anatomical details.

以条件化归一流为基础，提出了一个新的端到端影像域模型 AF2R，通过将众多可逆步骤连接起来，将上下文和运动估计引入到模型中以恢复作废运动伪影的 MRI 图像。

真实恢复者：MRI 运动伪影去除的无伪影流恢复器 (AF2R)

Realistic Restorer: artifact-free flow restorer(AF2R) for MRI motion  artifact removal

With the proliferation of deep learning methods, many computer vision
problems which were considered academic are now viable in the consumer setting.
One drawback of consumer applications is lossy compression, which is necessary
from an engineering standpoint to efficiently and cheaply store and transmit
user images. Despite this, there has been little study of the effect of
compression on deep neural networks and benchmark datasets are often losslessly
compressed or compressed at high quality. Here we present a unified study of
the effects of JPEG compression on a range of common tasks and datasets. We
show that there is a significant penalty on common performance metrics for high
compression. We test several methods for mitigating this penalty, including a
novel method based on artifact correction which requires no labels to train.

本文研究了 JPEG 压缩对常见计算机视觉任务和数据集的影响，发现高度压缩会显著损害图像识别性能，进而尝试采取几种方法来缓解该损害，其中包括一种不需标签训练的基于伪影校正的新方法。