Noise Adaption Network for Morse Code Image Classification

TL;DR

This paper introduces NANet, a two-stage neural network that improves Morse code image classification accuracy and robustness under various noise conditions by combining denoising and classification modules.

Contribution

The paper presents a novel two-stage Noise Adaptation Network (NANet) that effectively denoises and classifies Morse code images affected by diverse noise types, outperforming existing methods.

Findings

Enhanced classification accuracy across multiple noise types

Robustness to Gaussian, salt-and-pepper, and uniform noise

Superior performance compared to existing approaches

Abstract

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