采用多级残差滤波的非局部均值图像去噪方法

孙伟峰; 戴永寿

doi:10.11999/JEIT151227

采用多级残差滤波的非局部均值图像去噪方法

doi: 10.11999/JEIT151227

孙伟峰¹,
戴永寿¹

基金项目:

国家自然科学基金(61501520)，山东省自然科学基金(ZR2013FL035)，中央高校基本科研业务费专项资金(14CX02083A)

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出版历程
- 收稿日期: 2015-11-03
- 修回日期: 2016-03-04
- 刊出日期: 2016-08-19

Non-local Means Image Denoising with Multi-stage Residual Filtering

SUN Weifeng¹,
DAI Yongshou¹

Funds:

The National Natural Science Foundation of China (61501520), Shandong Provincial Natural Science Foundation (ZR2013FL035), The Fundamental Research Funds for the Central Universities (14CX02083A)

摘要

摘要: 为充分利用残差中的图像信息以提升非局部均值算法的去噪性能，该文提出一种多级残差图像滤波新方法。首先对含噪图像进行非局部均值滤波得到初始的去噪图像和权值分布矩阵，然后对残差图像进行固定权值非局部均值滤波来提取图像结构信息，将提取的信息经高斯平滑抑噪后作为补偿图像，与去噪图像相加得到增强的恢复图像。针对上述方法提出一种多级滤波的实现方案，从理论上推导证明了该方法的原理及可行性，并提出一种无需参考图像的迭代停止准则来自适应地优选滤波级数。实验结果表明，提出的迭代停止准则能够达到与峰值信噪比一致的优选结果；与经典的非局部均值算法相比，在计算效率相当的情况下，所提方法能够显著地提升其去噪性能，峰值信噪比平均可以提高1.2 dB，且具有更好的细节保持能力。
- 图像去噪 /
- 非局部均值 /
- 残差滤波 /
- 停止准则
Abstract: In order to sufficiently exploit the image information residing in the residual image for boosting the denoising performance of the Non-local Means (NLM) algorithm, a novel multi-stage residual filtering method is proposed. Firstly, the Non-Local Means algorithm is applied to a noisy image to produce an initial denoised image and a weight distributing matrix. Then the fixed-weight NLM algorithm is applied to the residual image followed by a Gaussian filtering process, which can extract the image content out from the residual as a compensation image. The compensation image is then added back to the denoised image to generate an enhanced restored image. An iterative scheme, whose principle and feasibility are derived and proved theoretically, is developed for the above filtering procedure; meanwhile a novel stopping criterion with no reference image required is proposed to determine the optimal number of iterations adaptively. Experimental results demonstrate that the proposed stopping criterion behaves similarly as the PSNR rule, and compared with the original NLM approach, the proposed method can boost the denoising performance significantly with 1.2 dB PSNR gains achieved on average and more detail information preserved, while the computational complexity is not apparently increased.
- Image denoising /
- Non-Local Means (NLM) /
- Residual filtering /
- Stopping criterion

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参考文献(22)

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