提出噪声感知注意力机制，提升牙科全景片去噪中细节恢复能力。
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
- 设计噪声感知自注意力模块，让模型聚焦噪声中的关键结构
- 在真实牙科片上实现更优的细部还原，提升诊断准确性
- 轻量级设计优于复杂模型，适合临床部署
卷积去噪自编码器在图像修复中表现强劲，但继承了卷积神经网络的固有缺陷：对低频特征（如平滑区域）恢复效果好，而对高频细节恢复较差。这在牙科全景片中尤为严重，因细微解剖结构对诊断至关重要。尽管自注意力机制可缓解此问题，但传统方法常偏向清晰区域，忽略噪声掩盖的关键信息。为此，本文提出噪声感知自注意力机制，使模型能在噪声区域仍有效关注并恢复关键特征。基于此，构建了面向牙科全景片的噪声感知注意力增强去噪自编码器（NAADA）。相比近期先进且更复杂的模型（如Uformer、MResDNN），NAADA在保持更优细节重建的同时，显著提升图像质量与诊断准确率。
原文摘要 · Abstract (English)
Convolutional denoising autoencoders (DAEs) are powerful tools for image restoration. However, they inherit a key limitation of convolutional neural networks (CNNs): they tend to recover low-frequency features, such as smooth regions, more effectively than high-frequency details. This leads to the loss of fine details, which is particularly problematic in dental radiographs where preserving subtle anatomical structures is crucial. While self-attention mechanisms can help mitigate this issue by emphasizing important features, conventional attention methods often prioritize features corresponding to cleaner regions and may overlook those obscured by noise. To address this limitation, we propose a noise-aware self-attention method, which allows the model to effectively focus on and recover key features even within noisy regions. Building on this approach, we introduce the noise-aware attention-enhanced denoising autoencoder (NAADA) network for enhancing noisy panoramic dental radiographs. Compared with the recent state of the art (and much heavier) methods like Uformer, MResDNN etc., our method improves the reconstruction of fine details, ensuring better image quality and diagnostic accuracy.
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