Ring artifacts in computed tomography images, arising from the undesirable
responses of detector units, significantly degrade image quality and diagnostic
reliability. To address this challenge, we propose a dual-domain regularization
model to effectively remove ring artifacts, while maintaining the integrity of
the original CT image. The proposed model corrects the vertical stripe
artifacts on the sinogram by innovatively updating the response inconsistency
compensation coefficients of detector units, which is achieved by employing the
group sparse constraint and the projection-view direction sparse constraint on
the stripe artifacts. Simultaneously, we apply the sparse constraint on the
reconstructed image to further rectified ring artifacts in the image domain.
The key advantage of the proposed method lies in considering the relationship
between the response inconsistency compensation coefficients of the detector
units and the projection views, which enables a more accurate correction of the
response of the detector units. An alternating minimization method is designed
to solve the model. Comparative experiments on real photon counting detector
data demonstrate that the proposed method not only surpasses existing methods
in removing ring artifacts but also excels in preserving structural details and
image fidelity.

提出了一种双域正则化模型来有效地消除计算机断层扫描图像中的环形伪影，并保持原始 CT 图像的完整性。该方法通过创新地更新探测器单元的响应不一致性补偿系数，以解决正弦图中的垂直条纹伪影，通过对条纹伪影应用组稀疏约束和投影视图方向稀疏约束。同时，对重建图像施加稀疏约束，进一步纠正图像域中的环形伪影。实验证明，该方法不仅在消除环形伪影方面超越了现有方法，而且在保持结构细节和图像保真度方面也表现出色。

X 光 CT 环形伪影去除的双域正则化方法

A Dual-domain Regularization Method for Ring Artifact Removal of X-ray  CT

We present a novel framework, called FrameNeRF, designed to apply
off-the-shelf fast high-fidelity NeRF models with fast training speed and high
rendering quality for few-shot novel view synthesis tasks. The training
stability of fast high-fidelity models is typically constrained to dense views,
making them unsuitable for few-shot novel view synthesis tasks. To address this
limitation, we utilize a regularization model as a data generator to produce
dense views from sparse inputs, facilitating subsequent training of fast
high-fidelity models. Since these dense views are pseudo ground truth generated
by the regularization model, original sparse images are then used to fine-tune
the fast high-fidelity model. This process helps the model learn realistic
details and correct artifacts introduced in earlier stages. By leveraging an
off-the-shelf regularization model and a fast high-fidelity model, our approach
achieves state-of-the-art performance across various benchmark datasets.

我们提出了一种名为 FrameNeRF 的新颖框架，旨在应用现成的快速高保真 NeRF 模型，以实现训练速度快且渲染质量高的少样本新视角合成任务。通过使用正则化模型作为数据生成器，从稀疏输入中生成密集视角，以提供快速高保真模型的后续训练。结合现成的正则化模型和快速高保真模型，我们的方法在各种基准数据集上取得了最先进的性能。