In this paper, we present our approach to the 3D CBCT Challenge 2024, a part
of ICASSP SP Grand Challenges 2024. Improvement in Cone Beam Computed
Tomography (CBCT) reconstruction has been achieved by integrating Swin Image
Restoration (SwinIR) based sinogram and image enhancement modules. The proposed
methodology uses Nesterov Accelerated Gradient Descent (NAG) to solve the least
squares (NAG-LS) problem in CT image reconstruction. The integration of
sinogram and image enhancement modules aims to enhance image clarity and
preserve fine details, offering a promising solution for both low dose and
clinical dose CBCT reconstruction. The averaged mean squared error (MSE) over
the validation dataset has decreased significantly, in the case of low dose by
one-fifth and clinical dose by one-tenth. Our solution is one of the top 5
approaches in this challenge.

该论文介绍了一种基于 SwinIR 的正弦图和图像增强模块相结合的 Nesterov 加速梯度下降方法，用于改善锥束计算机断层扫描的图像重建，实现了显著的降低均方误差并提高图像清晰度和保留细节的效果。

2024 年 3D CBCT 挑战赛：基于 SwinIR 的正弦图和图像增强改进的锥形束 CT 重建

3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using  SwinIR-Based Sinogram and Image Enhancement

Transformer-based methods have achieved impressive image restoration
performance due to their capacities to model long-range dependency compared to
CNN-based methods. However, advances like SwinIR adopts the window-based and
local attention strategy to balance the performance and computational overhead,
which restricts employing large receptive fields to capture global information
and establish long dependencies in the early layers. To further improve the
efficiency of capturing global information, in this work, we propose SwinFIR to
extend SwinIR by replacing Fast Fourier Convolution (FFC) components, which
have the image-wide receptive field. We also revisit other advanced techniques,
i.e, data augmentation, pre-training, and feature ensemble to improve the
effect of image reconstruction. And our feature ensemble method enables the
performance of the model to be considerably enhanced without increasing the
training and testing time. We applied our algorithm on multiple popular
large-scale benchmarks and achieved state-of-the-art performance comparing to
the existing methods. For example, our SwinFIR achieves the PSNR of 32.83 dB on
Manga109 dataset, which is 0.8 dB higher than the state-of-the-art SwinIR
method.

本文介绍了一种基于 Transformer 的方法，用于图像恢复，提出了一种名为 SwinFIR 的扩展方法，旨在提高捕捉全局信息的效率，同时改进了数据增强、预训练、特征集成等高级技术，使模型的性能得到显着提高，并在多个大型基准测试上取得了最先进的性能。

SwinFIR：通过快速傅里叶卷积和改进的训练重新审视 SwinIR 并用于图像超分辨率

SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and  Improved Training for Image Super-Resolution

Image restoration is a long-standing low-level vision problem that aims to
restore high-quality images from low-quality images (e.g., downscaled, noisy
and compressed images). While state-of-the-art image restoration methods are
based on convolutional neural networks, few attempts have been made with
Transformers which show impressive performance on high-level vision tasks. In
this paper, we propose a strong baseline model SwinIR for image restoration
based on the Swin Transformer. SwinIR consists of three parts: shallow feature
extraction, deep feature extraction and high-quality image reconstruction. In
particular, the deep feature extraction module is composed of several residual
Swin Transformer blocks (RSTB), each of which has several Swin Transformer
layers together with a residual connection. We conduct experiments on three
representative tasks: image super-resolution (including classical, lightweight
and real-world image super-resolution), image denoising (including grayscale
and color image denoising) and JPEG compression artifact reduction.
Experimental results demonstrate that SwinIR outperforms state-of-the-art
methods on different tasks by $\textbf{up to 0.14$\sim$0.45dB}$, while the
total number of parameters can be reduced by $\textbf{up to 67%}$.

本研究提出了一种基于 Swin Transformer 的强基线模型 SwinIR，用于图像恢复，通过对三种典型任务进行实验（图像超分辨率、图像降噪和 JPEG 压缩），实验结果表明，SwinIR 在不同任务上的表现比最先进的方法提高了至多 0.14~0.45dB，同时可以将参数总数减少高达 67%。