一种基于光滑波纹变换和卷积神经网络的红外图像新的隐形图像方法
Yu Bai1, Li Li1, Jianfeng Lu1
1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种用于红外图像版权保护的新框架,使用具有光滑波形变换 (SWT) 和挤压刺激 (SE) 的卷积神经网络预测器 (CNNP). 该方法有效地减少了像素预测错误,提高了隐形图谱的性能.
科学领域:
- 计算机视觉 计算机视觉
- 数字图像处理 数字图像处理
- 信息安全 信息安全
背景情况:
- 红外图像对于目标检测和现场监控至关重要,需要强有力的版权保护.
- 现有的图像隐形图像算法通常依赖于像素预测错误,使错误减少对有效性至关重要.
研究的目的:
- 提出一个新的框架,SSCNNP,用于红外图像版权保护.
- 通过提高红外图像的预测精度来增强隐形图像.
- 开发一个计算效率高的红外图像隐形图像模型.
主要方法:
- 开发了一个卷积神经网络预测器 (CNNP) 框架,SSCNNP,将卷积神经网络 (CNN) 与光滑波形转换 (SWT) 集成.
- 超分辨率卷积神经网络 (SRCNN) 和SWT用于预处理.
- 一个注意力机制,特别是Squeeze-Excitation (SE) 注意力,被纳入来提高预测准确性.
主要成果:
- 拟议的算法通过利用空间和频率域特征,显著减少了像素预测错误.
- 实验结果显示,与现有方法相比,无感知度和水印能力有所改善.
- 该算法以相同的水标容量实现了0.17的平均PSNR改进.
结论:
- 红外图像版权保护的SSCNNP提供了一个有效的解决方案.
- 该模型在不需要昂贵的硬件或大量存储的情况下展示了高性能.
- 整合CNN,SWT和SE的注意力为先进的隐藏图提供了一个强大的方法.
关键词:
这是一个基于CNN的预测器.在CNNP中,CNNP是CNNP.这是一个SRCNNNN.这就是为什么SWT是SWT.卷积神经网络是一种卷积神经网络.红外图像中的红外图像.石摄影 (steganography) 是一种石摄影技术.更多相关视频
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