Singular Value Decomposition Band-Pass-Filter for Image Background Suppression and Denoising

HU Mou-fa; DONG Wen-juan; WANG Shu-hong; CHEN Zeng-ping

您当前的位置：

首页 >

文章列表页 >

Singular Value Decomposition Band-Pass-Filter for Image Background Suppression and Denoising

更新时间：2025-07-16

- Singular Value Decomposition Band-Pass-Filter for Image Background Suppression and Denoising
- Acta Electronica Sinica Vol. 36, Issue 1, Pages: 111-116(2008)
- 作者机构：
  
  国防科学技术大学ATR重点实验室,湖南,长沙,410073
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.73
- Published：2008
- 稿件说明：
移动端阅览
HU Mou-fa, DONG Wen-juan, WANG Shu-hong, et al. Singular Value Decomposition Band-Pass-Filter for Image Background Suppression and Denoising[J]. Acta Electronica Sinica, 2008, 36(1): 111-116.
DOI：

HU Mou-fa, DONG Wen-juan, WANG Shu-hong, et al. Singular Value Decomposition Band-Pass-Filter for Image Background Suppression and Denoising[J]. Acta Electronica Sinica, 2008, 36(1): 111-116. DOI：

摘要

针对可见光图像弱小目标检测中的背景抑制和去噪问题

提出了奇异值分解(Singular Value Decomposition

SVD)带通滤波新方法.首先分析了图像奇异值与目标、噪声和图像背景的关系

结果表明奇异值的高序部分更多地反映图像噪声

中序部分更多地反映目标性质

而低序部分更多地反映图像背景.以此为依据提出了SVD-I型和SVD-II型两种带通滤波器

并给出了奇异值曲线转折点法和门限准则法两种滤波器参数确定方法.实验表明SVD带通滤波能有效抑制图像背景

去除噪声

进而提高弱小目标的信噪比.

Abstract

A new singular value decomposition (SVD) band-pass-filter technology is presented for background suppression and denoising in small targets detecting of visible images.Firstly

the relation between image singular value and targets

image noise and image background is analyzed.And results show that the high order part of image singular value obtains more information of image noise

the middle order part obtains more information of targets and the low order part obtains more information of image background.Based on this fact

two SVD band-pass-filters named SVD-I and SVD-II are proposed.And two filter parameters estimation methods are given

including singular value curve turning-point method and threshold criterion method.Experiments show that the new SVD band-pass-filter can suppress image background and denoising effectively

and improve the signal to noise (SNR) of small targets.

关键词

Keywords

references

Views

2147

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation

Image Denoising Using Subsquarewise Threshold in Bandelets Domain

The Lattice Boltzmann Method Based Image Denoising

Image Poisson Denoising Using Sparse Representations

Construction of Non-Aliasing Pyramidal Transform

Related Author

LI Xiao-yao

WANG Lian-hong

ZHOU Yi-cong

ZHANG Jing

ZhANG Xiang-rong

JIAO Li-cheng

LIU Fang

Related Institution

College of Electrical and Information Engineering, Hunan University

Department of Computer and Information Science, University of Macau, Macau

西安电子科技大学智能信息处理研究所

智能感知与图像理解教育部重点实验室

1. 西安电子科技大学计算机学院

⁰