拒绝RGB和GS图像的瓦尼拉自动编码器,具有高斯噪声
Armando Adrián Miranda-González1, Alberto Jorge Rosales-Silva1, Dante Mújica-Vargas2
1Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, Mexico.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种Denoising Vanilla自编码 (DVA) 架构,用于图像中的高斯反解. DVA方法有效地抑制噪音,优于现有的神经网络方法,用于更清洁的图像处理.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 由于噪音造成的图像质量下降是计算机视觉和监控等各种应用中的一个重大挑战.
- 外部因素可能会改变捕获的图像数据,导致信息丢失并需要数据恢复方法.
- 强大的图像处理需要数据准确地表示现实世界的场景,使噪音抑制至关重要.
研究的目的:
- 为高斯式denoising提出一个新的Denoising Vanilla自编码 (DVA) 架构.
- 通过恢复更接近原始场景的数据信息来增强图像处理.
- 评估DVA架构的性能与色彩和灰度图像的最先进方法相比.
主要方法:
- 开发一个Denoising Vanilla自编码 (DVA) 架构.
- 使用无监督的神经网络进行denoising过程.
- 在验证和高分辨率噪音图像集上使用客观的数值结果进行性能评估.
主要成果:
- 拟议的DVA架构在高斯解密方面表现出优越的性能,与现有方法相比.
- 客观的数值结果证实了DVA方法的有效性.
- DVA方法成功地抑制了色彩和灰度图像中的噪音.
结论:
- 否定瓦尼拉自编码 (Denoising Vanilla Autoencoding,DVA) 架构是高斯图像否定的有效解决方案.
- 这种无监督神经网络方法比当前最先进的技术提供了显著的改进.
- 通过恢复图像质量,DVA方法有助于更强大的图像处理系统.
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