An Improved GSR Image Denoising Method

MA Jie; WU Li-tao; ZHANG Xiao-yan

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An Improved GSR Image Denoising Method

更新时间：2026-09-04

- An Improved GSR Image Denoising Method
- Vol. 34, Issue 6, Pages: 99-103(2017)
- 作者机构：
  
  1. 河北工业大学电子信息工程学院,天津,300401
  2. 天津市电子材料与器件重点实验室,天津,300401
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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马杰, 武利涛, 张晓严. 一种改进的组稀疏表示图像去噪方法[J]. 微电子学与计算机, 2017, 34(6): 99-103.

MA Jie, WU Li-tao, ZHANG Xiao-yan. An Improved GSR Image Denoising Method[J]. 2017, 34(6): 99-103.
马杰, 武利涛, 张晓严. 一种改进的组稀疏表示图像去噪方法[J]. 微电子学与计算机, 2017, 34(6): 99-103. DOI：

MA Jie, WU Li-tao, ZHANG Xiao-yan. An Improved GSR Image Denoising Method[J]. 2017, 34(6): 99-103. DOI：

摘要

研究了一种匹配梯度分布的组稀疏表示图像去噪模型

将相似图像块构成的结构组作为稀疏表示单元并加入梯度直方图保持正则项匹配梯度分布

基于非精确增广拉格朗日乘子法进行求解得到恢复图像.仿真实验结果表明

该方法不仅可以减少图像处理的时间并可在有效去除噪声的同时保持图像精细纹理结构

获得了较高的峰值信噪比和结构相似性索引测度.在需要得到图像丰富细节的情况下

该方法具有实用价值和现实意义.

Abstract

A matching gradient distribution group based sparse representation model is researched

in which the basic unit of sparse representation is the group composed by nonlocal patches with similar structures

simultaneously the gradient histogram preserving regularization is added to match gradient distribution

and imprecise Augmented Lagrange multiplier method is used to solve the model. It is well shown by the results that this method can not only shorten the time of image processing

but also retain the fine or small-scale texture structure as well as denoising effectively

and obtain higher output PSNR and SSIM than some current state-of-the-art schemes. In case of the rich details of the image is needed

this method has practical value and realistic significance.

关键词

Keywords

references

Zhang J, Ma S, Zhang Y, et al. Image deblocking using groupbased sparse representation and quantization constraint prior[C]//Proc. IEEE Int. Conf. Image Process. IEEE, 2015: 306-310.

Dong W, Zhang L, Shi G, et al. Nonlocally centralized sparse representation for lmage restoration[J]. IEEE Trans. on Image Processing, 2013, 22(4), 1620-1630. DOI: 1620

Zhou M. Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images[J]. IEEE Trans. Image Processing, 2012, 21(1): 130-144. DOI: 130

Beijing Gonzalez R C, WoodsR E. Digital image processing[M]. Beijing: Publishing House of Electronics Industry, 2005.

Zhang J, Zhao D, Gao W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351. DOI: 3336

Zuo W, Zhang L, Song C, et al. Texture enhanced image denoising via gradient histogram preservation[C]//Proc. Int. Conf. CVPR. IEEE, 2013: 1203-1210.

Zhao M, Li H. Image denoising based on Poisson-like noise model[J]. Chinese Journal of Stereology and Image Analysis, 2011, 16(17): 32-38. DOI: 32

Zuo W, Zhang L, Song C, et al. Gradient histogram estimation and preservation for texture enhanced image denoising[J]. IEEE Trans. Image Process, 2014, 23(6): 2459-2472. DOI: 2459

Lin Z C, Chen M M, Wu L Q, et al. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices[R]. UIUC, 2010.

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Related Author

CHEN Li-xia

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Related Institution

1 School of Mathematics and Computing Science, Guilin University of Electronic Technology

2 Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology

3 Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology

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