Image Denoising Algorithm Based on Nonlocally Sparse Representation and Group

CHEN Li-xia; SAI Peng-fei

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Image Denoising Algorithm Based on Nonlocally Sparse Representation and Group

更新时间：2026-09-04

- Image Denoising Algorithm Based on Nonlocally Sparse Representation and Group
- Vol. 32, Issue 11, Pages: 185-188(2015)
- 作者机构：
  
  1. 1 桂林电子科技大学机构数学与计算科学学院,广西,桂林,541004
  2. 2 桂林电子科技大学广西高校图像图形智能处理重点实验室,广西,桂林,541004
  3. 3 桂林电子科技大学广西密码学与信息安全重点实验室,广西,桂林,541004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2015
- 稿件说明：
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陈利霞, 赛朋飞. 组约束与非局部稀疏的图像去噪算法[J]. 微电子学与计算机, 2015, 32(11): 185-188.

CHEN Li-xia, SAI Peng-fei. Image Denoising Algorithm Based on Nonlocally Sparse Representation and Group[J]. 2015, 32(11): 185-188.
陈利霞, 赛朋飞. 组约束与非局部稀疏的图像去噪算法[J]. 微电子学与计算机, 2015, 32(11): 185-188. DOI：

CHEN Li-xia, SAI Peng-fei. Image Denoising Algorithm Based on Nonlocally Sparse Representation and Group[J]. 2015, 32(11): 185-188. DOI：

摘要

现有的非局部稀疏表示去噪算法大多严格依赖于块匹配

且其去噪性能受制于匹配的相似块的数量.鉴于此

提出了组约束与非局部稀疏的图像去噪模型.模型在非局部稀疏的基础上加入了分组约束

增强了图像块之间的非局部相似度

块匹配更加精确.实验表明

模型无论是在视觉效果还是峰值信噪比上均具有较好的性能.

Abstract

The most existing denoising algorithms based on nonlocal sparse representation are strictly dependent on patch matching

and the denoising performance is subject to the numbers of similar patches. So a image denoising algorithm based on nonlocally sparse representation and group is proposed. The group-based constraints is introduced to the nonlocal sparse representation

which can enhance the nonlocal similarity between image patches and the patch matching is more accurate. Experiments show that the model has a good performance in both visual effect and peak signal to noise ratio.

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