Image Denoising Using Subsquarewise Threshold in Bandelets Domain

ZHANG Wen-ge; LIU Fang; JIAO Li-cheng; ZhANG Xiang-rong; BR

您当前的位置：

首页 >

文章列表页 >

Image Denoising Using Subsquarewise Threshold in Bandelets Domain

更新时间：2025-07-16

- Image Denoising Using Subsquarewise Threshold in Bandelets Domain
- Acta Electronica Sinica Vol. 38, Issue 2, Pages: 290-294(2010)
- 作者机构：
  
  1. 1. 西安电子科技大学计算机学院,陕西,西安,710071
  2. 智能感知与图像理解教育部重点实验室,陕西,西安,710071
  3. 西安电子科技大学智能信息处理研究所,陕西,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published：2010
- 稿件说明：
移动端阅览
ZHANG Wen-ge, LIU Fang, JIAO Li-cheng, et al. Image Denoising Using Subsquarewise Threshold in Bandelets Domain[J]. Acta Electronica Sinica, 2010, 38(2): 290-294.
DOI：

ZHANG Wen-ge, LIU Fang, JIAO Li-cheng, et al. Image Denoising Using Subsquarewise Threshold in Bandelets Domain[J]. Acta Electronica Sinica, 2010, 38(2): 290-294. DOI：

摘要

把正交bandelets域经四叉树分割的每一子块系数建模为广义高斯分布，基于Bayesian框架，推导出了自适应逐子块局部阈值的计算公式，找出了其参数分布的最佳范围，提出了图像bandelets域的逐子块阈值去噪算法。由于充分利用了图像的局部统计信息，所提算法对自然图像去噪的视觉效果和评价指标都好于BayesShrink和其它基于阈值的去噪算法。

Abstract

By modeling orthogonal bandelets coefficients in each quad-tree subsquare as Generalized Gaussian Distribution model

a calculation formula for adaptive local subsquarewise threshold is derived under the Bayesian frame

and the best range of the parameter needed to calculate subsquarewise threshold is found out. On these basis

a subsquarewise threshold denoising algorithm for natural images is proposed in bandelets domain. Owing to making full use of local statistic information of the image

the visual effect and evaluation criteria of proposed algorithm for natural image denoising outperform that of BayesShrink and other threshold-based methods.

关键词

Keywords

references

Views

1425

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Image Dehazing Based on Gradient Guided Polarization Degree Estimation

DRHA-UIE: An Underwater Image Enhancement Method Based on Dual Residual Hybrid Attention Block

Image Harmonization Guided by Semantic Information

MalMKNet: A Multi-Scale Convolutional Neural Network Used for Malware Classification

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

Related Author

XU Wan-chun

ZHANG Yan

ZHANG Jing-hua

LING Feng

LI Shun

WANG Xin

SHI Hui

YANG Zi-yuan

Related Institution

National Key Laboratory of Science and Technology on Automatic Target Recognition， College of Electronic Science and Technology， National University of Defense Technology

Academy of Military Science

College of Electronic Science and Technology， National University of Defense Technology

College of Computer Science and Technology， Jilin University

School of Communication and Information Engineering， Chongqing University of Posts and Telecommunications

⁰