标题： 深度学习架构在医学图像去噪中的应用：CNN-DAE、CADTra 和 DCMIEDNet 的比较研究 显示英文标题

标题： Deep Learning Architectures for Medical Image Denoising: A Comparative Study of CNN-DAE, CADTra, and DCMIEDNet

摘要： 医学成像模态固有地容易受到噪声污染，这会降低诊断效用和临床评估的准确性。 本文对三种最先进的深度学习架构在MRI脑图像去噪中的表现进行了全面比较评估：CNN-DAE、CADTra和DCMIEDNet。 我们使用Figshare MRI脑数据集，在多个高斯噪声强度（$\sigma = 10, 15, 25$）下系统评估了这些模型。 我们的实验结果表明，DCMIEDNet在较低噪声水平下表现出优越的性能，分别在$\sigma = 10$和$15$下的PSNR值为$32.921 \pm 2.350$dB和$30.943 \pm 2.339$dB。 然而，在严重噪声条件下（$\sigma = 25$），CADTra表现出更大的鲁棒性，达到了最高的PSNR值$27.671 \pm 2.091$dB。 所有深度学习方法均显著优于传统的基于小波的方法，在测试条件下改进幅度为5-8 dB。 本研究建立了医学图像去噪的定量基准，并提供了针对不同噪声强度的架构特定优势的见解。 显示英文摘要

摘要： Medical imaging modalities are inherently susceptible to noise contamination that degrades diagnostic utility and clinical assessment accuracy. This paper presents a comprehensive comparative evaluation of three state-of-the-art deep learning architectures for MRI brain image denoising: CNN-DAE, CADTra, and DCMIEDNet. We systematically evaluate these models across multiple Gaussian noise intensities ($\sigma = 10, 15, 25$) using the Figshare MRI Brain Dataset. Our experimental results demonstrate that DCMIEDNet achieves superior performance at lower noise levels, with PSNR values of $32.921 \pm 2.350$ dB and $30.943 \pm 2.339$ dB for $\sigma = 10$ and $15$ respectively. However, CADTra exhibits greater robustness under severe noise conditions ($\sigma = 25$), achieving the highest PSNR of $27.671 \pm 2.091$ dB. All deep learning approaches significantly outperform traditional wavelet-based methods, with improvements ranging from 5-8 dB across tested conditions. This study establishes quantitative benchmarks for medical image denoising and provides insights into architecture-specific strengths for varying noise intensities.