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边缘和纹理感知图像消噪使用中间噪声残留U-net与手工制作的功能

Soniya S¹, Sriharipriya K C¹

¹School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

PeerJ. Computer science

|February 3, 2025

概括

这项研究介绍了中位噪声残留U-Net (MNRU-Net) 以实现有效的图像消噪. 该模型通过手工制作的功能和噪声估计来增强传统的U-Net,优于具有较低计算成本的复杂方法.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

传统的图像无声化通常仅专注于峰值信号与噪声比 (PSNR),忽视边缘特征.
完全卷积神经网络 (CNN) 面临着高噪音水平,需要大量的数据集,增加计算和内存需求.

研究的目的:

提出一个新的图像染模型,中位噪声残留U-Net (MNRU-Net),解决现有方法的局限性.
提高图像重建质量,特别是在较高的噪音水平下,使用有限的训练数据集并且没有图像增强.

主要方法:

该MNRU-Net将手工制作的功能集成到传统U-Net架构的输入层中.
它结合了从中位过和图像梯度信息中得出的近似噪声估计.
该模型使用跳过连接来提高在较高噪声标准偏差下性能.

主要成果:

与RDDCNN (RDDCNN) 等复杂的脱光模型相比,MNRU-Net表现出更好的性能.
使用PSNR,结构相似性和优点数字的评估显示,在15,25和50的噪声标准偏差中,有显著的改善.
拟议的架构有效地保留了边缘特征,并准确地重建图像,即使有大量噪音.

结论:

该MNRU-Net提供了一种高效和有效的解决方案,用于图像消噪,特别是在噪音水平高的场景中.
手工制作的功能和基于中位数的噪声估计的集成增强了U-Net的无噪声能力.
这种简单而强大的架构实现了高性能,降低了计算复杂性和内存需求.

关键词:

渐变信息渐变信息手工制作的特点是手工制作的特点.这是一个MNRU-Net.跳过连接跳过连接

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