计算机工程 ›› 2013, Vol. 39 ›› Issue (3): 241-244.doi: 10.3969/j.issn.1000-3428.2013.03.047

• 图形图像处理 • 上一篇    下一篇

基于形状自适应PCA的三维块匹配图像去噪

杨 娟1,贾振红1,覃锡忠1,杨 杰2,胡英杰3   

  1. (1. 新疆大学信息科学与工程学院,乌鲁木齐 830046; 2. 上海交通大学图像处理与模式识别研究所,上海 200240; 3. 新西兰奥克兰理工大学知识工程与发现研究所,新西兰 奥克兰 1020)
  • 收稿日期:2012-04-16 出版日期:2013-03-15 发布日期:2013-03-13
  • 作者简介:杨 娟(1987-),女,硕士研究生,主研方向:图像处理;贾振红,教授、博士;覃锡忠,副教授;杨 杰,教授、博士;胡英杰,博士
  • 基金项目:
    科技部国际科技合作基金资助项目(2009DFA12870);教育部促进与美大地区科研合作与高层次人才培养基金资助项目

BM3D Image Denoising Based on Shape-adaptive Principal Component Analysis

YANG Juan 1, JIA Zhen-hong 1, QIN Xi-zhong 1, YANG Jie 2, HU Ying-jie 3   

  1. (1. College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China; 2. Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China; 3. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand)
  • Received:2012-04-16 Online:2013-03-15 Published:2013-03-13

摘要: 三维块匹配法中3-D变换真实信号的稀疏表达能力较弱,针对该问题,提出一种关于图像去噪的三维块匹配(BM3D)改进算法。采用形状自适应的图像块(邻域)代替BM3D算法中的平方窗图像块,对3-D变换处理的形状自适应图像块进行PCA变换。实验结果证明,该算法能够有效去除图像的高斯噪声,提高图像的峰值性噪比和结构相似度,且在保持图像的边缘等细节信息方面性能较好,图像视觉效果有明显改善。

关键词: 形状自适应图像块, 主成分分析, 三维变换处理, 稀疏性, 图像去噪

Abstract: Aiming at the problem of the weak ability of the 3-D transform to sparsely represent the true-image data in Block Matching 3D(BM3D) algorithm, an improved algorithm of image denoising by sparse BM3D is proposed. This paper uses shape-adaptive image patches(neighborhoods) replacing square windows image block. Shape-adaptive image patches by 3-D transform processing are PCA transformed. Experimental results show that the proposed method can efficiently denoise the Gaussian noise and improve the PSNR and SSIM of image, especially in preserving image details and introducing very few artifacts.

Key words: shape-adaptive image block, Principal Component Analysis(PCA), 3-D transform processing, sparsity, image denoising

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