提出噪声自适应网络，提升多类噪声下摩尔斯电码图像分类准确率。
Noise Adaption Network for Morse Code Image Classification
- 分两阶段：先用U型网络去噪并提取抗噪特征，再分类
- 在高斯、椒盐、均匀噪声下准确率达98.7%以上
- 适合需要抗干扰识别的通信安全与工业检测场景
信息安保的重要性日益凸显，加密技术在保护通信内容方面发挥关键作用。摩尔斯电码作为一种成熟有效的加密方式，广泛应用于电报通信及多个领域。然而，摩尔斯电码图像在传输过程中常受多种噪声和失真影响，导致分类效果受限。现有方法多仅针对单一噪声类型进行分类，忽略实际中复杂的噪声组合情况。为此，我们提出一种新型两阶段方法——噪声自适应网络（NANet），在纯净图像上训练，通过提取对噪声不敏感的关键信息，实现对噪声图像的适应性分类。第一阶段采用U型网络结构学习代表性特征并实现去噪；第二阶段使用深度卷积神经网络完成分类。利用第一阶段的去噪模块，显著提升后续分类的精度与鲁棒性。我们在包含高斯、椒盐、均匀噪声的多样化数据集上进行了评估，结果表明该方法优于现有技术。数据集已开源：https://github.com/apple1986/MorseCodeImageClassify
原文摘要 · Abstract (English)
The escalating significance of information security has underscored the per-vasive role of encryption technology in safeguarding communication con-tent. Morse code, a well-established and effective encryption method, has found widespread application in telegraph communication and various do-mains. However, the transmission of Morse code images faces challenges due to diverse noises and distortions, thereby hindering comprehensive clas-sification outcomes. Existing methodologies predominantly concentrate on categorizing Morse code images affected by a single type of noise, neglecting the multitude of scenarios that noise pollution can generate. To overcome this limitation, we propose a novel two-stage approach, termed the Noise Adaptation Network (NANet), for Morse code image classification. Our method involves exclusive training on pristine images while adapting to noisy ones through the extraction of critical information unaffected by noise. In the initial stage, we introduce a U-shaped network structure designed to learn representative features and denoise images. Subsequently, the second stage employs a deep convolutional neural network for classification. By leveraging the denoising module from the first stage, our approach achieves enhanced accuracy and robustness in the subsequent classification phase. We conducted an evaluation of our approach on a diverse dataset, encom-passing Gaussian, salt-and-pepper, and uniform noise variations. The results convincingly demonstrate the superiority of our methodology over existing approaches. The datasets are available on https://github.com/apple1986/MorseCodeImageClassify
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