A New Video Image Sharpness Method by Recognizing Pixel Characteristics

YUAN Ye

Chinese

您当前的位置：

首页 >

文章列表页 >

A New Video Image Sharpness Method by Recognizing Pixel Characteristics

更新时间：2026-09-04

- A New Video Image Sharpness Method by Recognizing Pixel Characteristics
- Vol. 30, Issue 9, Pages: 34-36(2013)
- 作者机构：
  
  上海对外贸易学院商务信息学院,上海,201620
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
移动端阅览
袁野. 一种识别图像点性质的视频图像锐化算法[J]. 微电子学与计算机, 2013, 30(9): 34-36.

YUAN Ye. A New Video Image Sharpness Method by Recognizing Pixel Characteristics[J]. 2013, 30(9): 34-36.
袁野. 一种识别图像点性质的视频图像锐化算法[J]. 微电子学与计算机, 2013, 30(9): 34-36. DOI：

YUAN Ye. A New Video Image Sharpness Method by Recognizing Pixel Characteristics[J]. 2013, 30(9): 34-36. DOI：

摘要

针对数字电视视频后处理芯片中现有锐化技术中处理后的图像会放大噪声的缺点，提出了一种通过识别图象点性质来同时去噪并锐化的处理方法。首先提取其中的高频分量，根据高频分量的不同将图像点划分为噪声，细节、小边缘、大边缘、超大边缘等几种分类，然后先平滑掉噪声信号，再分别对细节和各种边缘进行不同程度的增强。实验仿真结果表明该方法既可以消除噪声，细节和小边缘、大边缘得到不同程度地增强，超大边缘却没有白边出现，大幅度提高了图像的清晰度。

Abstract

A new sharp method by recognizing pixel characteristics is presented in order to reduce noise sensitivity of traditional unsharp masking technique.Every pixel of the image is classified into noise

detail

small edge

big edge and huge edge point according to different high frequency component.Then de-noising operation is performed to noise pixel.The sharpening factor varies automatically based on high frequency component.Experimental results show that image noise can be reduced

image sharpness can be improved effectively without edge overshoot.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Monte carlo analysis of V-layout trench SiC neutron detector

Design of ultra-low power real-time clock circuits

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Microservices anomaly detection based on the correlation of monitoring metrics

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰