对比三种深度网络在脑部MRI去噪中的表现，发现不同模型各有优势。
Deep Learning Architectures for Medical Image Denoising: A Comparative Study of CNN-DAE, CADTra, and DCMIEDNet
- 比较CNN-DAE、CADTra和DCMIEDNet三种网络结构的去噪能力。
- 低噪声下DCMIEDNet表现最优，高噪声下CADTra更稳定，均显著优于传统方法。
- 适合医疗影像处理研究者参考，尤其关注噪声强度影响的场景。
医学成像固有噪声会降低诊断价值与评估准确性。本文系统比较了三种先进的深度学习架构在脑部MRI去噪中的表现：CNN-DAE、CADTra和DCMIEDNet。实验在Figshare MRI Brain Dataset上进行，针对高斯噪声强度σ=10、15、25进行测试。结果表明，DCMIEDNet在低噪声条件下表现更优，σ=10和σ=15时分别达到32.921±2.350 dB和30.943±2.339 dB的PSNR；而当σ=25时，CADTra表现出更强鲁棒性，取得最高PSNR值27.671±2.091 dB。所有深度学习方法均显著优于传统小波去噪，性能提升达5–8 dB。本研究建立了量化基准，揭示了不同架构在不同噪声强度下的适用特性。
原文摘要 · Abstract (English)
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 ($σ= 10, 15, 25$) 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 $σ= 10$ and $15$ respectively. However, CADTra exhibits greater robustness under severe noise conditions ($σ= 25$), 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.
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