计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2588-2591.DOI: 10.11772/j.issn.1001-9081.2013.09.2588

• 多媒体处理技术 • 上一篇    下一篇

基于高斯加权和流形的高保真彩色图像降噪

陈中秋1,石锐1,刘晶淼2   

  1. 1. 重庆大学 计算机学院,重庆 400030;
    2. 中国气象局 沈阳大气环境研究所,沈阳 100041
  • 收稿日期:2013-02-04 修回日期:2013-03-15 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 陈中秋
  • 作者简介:石锐(1968-),女,重庆人,副教授,博士,CCF会员,主要研究方向:模式识别、数字图像处理;
    陈中秋(1988-),男,重庆人,硕士研究生,主要研究方向:模式识别、数字图像处理;
    刘晶淼(1956-),男,辽宁沈阳人,研究员,博士,主要研究方向:大气环境及陆面过程和气候诊断与预测。
  • 基金资助:

    科技部科研院所技术开发研究专项

Color image denoising based on Gaussian weighting and manifold for high fidelity

CHEN Zhongqiu1,SHI Rui1,LIU Jingmiao2   

  1. 1. School of Computer Science, Chonqing University, Chongqing 400030, China
    2. Institute of Atmospheric Environment, China Meteorological Administration, Shenyang Liaoning 100041, China
  • Received:2013-02-04 Revised:2013-03-15 Online:2013-10-18 Published:2013-09-01
  • Contact: CHEN Zhongqiu

摘要: 针对用矢量法对彩色图像进行降噪处理,算法复杂度较高,无法达到实时处理的问题,提出了基于改进高斯加权和自适应流形的高保真彩色图像降噪方法。首先,将彩色图像用非局部均值算法得到高维数据,使用改进的高斯内核对彩色图像进行加权计算;然后,采用抛雪球方法处理这些高维数据,以高斯距离为权值,投影每个像素点的颜色到自适应流形;接着,对流形进行平滑降维,采用迭代法实现图像平滑;最后,收集流形中的平滑值,将平滑值对所有像素进行插值,得到降噪后的图像数据。实验证明,该方法对彩色图像进行降噪处理后,能够很好地保留原图像的细节,不会掺杂周围像素的颜色,算法处理速度较快,能够达到实时处理效果,降噪效果与原算法相比峰值信噪比(PSNR)提高近2.0dB,结构相似度提高了1百分点以上。

关键词: 彩色图像, 高斯加权, 非局部均值, 自适应流形, 高维滤波器, 图像降噪

Abstract: Using vector method for color image denoising, the complexity of the algorithm is high and cannot achieve real-time performance. A method for high fidelity color image denoising was proposed based on Gaussian weighting and adaptive manifold. Firstly, it used the non-local means algorithm to get high-dimensional data, and used the improved Gaussian kernel to calculate the weight of color image. Secondly, splatting method was used to deal with the high-dimensional data, and a Gaussian distance-weighted projection of the colors of all pixels was performed onto each adaptive manifold. Thirdly, smooth dimensionality reduction was done on convection shape, and iterative method was used for image smoothing. Finally, the final filter response was computed for each pixel by interpolating blurred values gathered from all adaptive manifolds. The experimental results show that the algorithm has a superior denoising performance than the original one, and it also can improve real-time performance. By using this algorithm, the details can be preserved well. Peak Signal-to-Noise Ratio (PSNR) can be improved nearly 2.0dB, and Structural Similarity Index Measurement (SSIM) can be improved more than 1%.

Key words: color image, Gaussian weighting, non-local means, adaptive manifold, high-dimensional filter, image denoising

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