Blind Video Image Denoising Based on Nonlocal Self-Similarity Series Sets

XING Yuan-xiu; LI Jun-xian; WANG Wen-bo; WANG Song

doi:10.12263/DZXB.20191207

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Blind Video Image Denoising Based on Nonlocal Self-Similarity Series Sets

PAPERS | 更新时间：2025-12-08

- Blind Video Image Denoising Based on Nonlocal Self-Similarity Series Sets
- ACTA ELECTRONICA SINICA Vol. 49, Issue 8, Pages: 1498-1506(2021)
- 作者机构：
  
  1.武汉科技大学理学院，湖北武汉 430081
  2.冶金工业过程系统科学湖北省重点实验室，湖北武汉 430081
  3.南卡罗莱纳大学工程与计算学院，美国南卡罗莱纳州哥伦比亚市 29208
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20191207
  CLC： TP391;
- Received：25 October 2019，
  
  Revised：2021-02-24，
  
  Published：25 August 2021
- 稿件说明：
移动端阅览
邢远秀,李军贤,王文波等.基于非局部自相似序列集的一类视频图像盲去噪算法[J].电子学报,2021,49(08):1498-1506.

XING Yuan-xiu,LI Jun-xian,WANG Wen-bo,et al.Blind Video Image Denoising Based on Nonlocal Self-Similarity Series Sets[J].ACTA ELECTRONICA SINICA,2021,49(08):1498-1506.
邢远秀,李军贤,王文波等.基于非局部自相似序列集的一类视频图像盲去噪算法[J].电子学报,2021,49(08):1498-1506. DOI： 10.12263/DZXB.20191207.

XING Yuan-xiu,LI Jun-xian,WANG Wen-bo,et al.Blind Video Image Denoising Based on Nonlocal Self-Similarity Series Sets[J].ACTA ELECTRONICA SINICA,2021,49(08):1498-1506. DOI： 10.12263/DZXB.20191207.

摘要

为提高具有帧间位移平移特性的视频图像的信噪比和去噪时效，本文提出了基于非局部自相似序列集的视频图像盲去噪算法.选取与待去噪视频图像前后相邻的若干图像帧，在每一图像帧中寻找具有典型特征的图像块群，并通过在前一帧图像中查找和该图像块群具有最小差异度的块群来确定帧间的精确位移；将待去噪视频图像划分成若干图像块，根据帧间位移快速构建每个图像块的自相似序列集；随后将每个自相似序列集中的二维图像块整合成三维矩阵后进行三维变换，并对变换系数进行自适应阈值处理；再将三维逆变换后的图像块融合生成去噪图像.实验结果表明，在噪声方差未知的情况下，本文算法所得去噪视频图像具有较好的信噪比和视觉效果，并且有较高的运行效率.

Abstract

A blind video image denoising algorithm based on non-local self-similar series sets is proposed to improve the peak signal-to-noise ratio and denoising efficiency of video images with displacement characteristics. Image block-groups with typical features are detected in each frame

and accurate inter-frame displacement between the image and previous frame is computed based on image block-matching. The noise image is divided into several image blocks

and the self-similar series set of each image blocks is constructed quickly according to the inter-frame displacement. A 3D transform is applied to the self-similar series set

followed by an adaptive-threshold of the transform coefficients to attenuate the noise. The 3D estimate after inverse 3D transformation are aggregated to obtain the finial image. Experimental results show that the proposed algorithm has significate advantages in PSNR and visual effect in unknown noise variance

meanwhile it also has higher efficiency.

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references

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