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采用多級殘差濾波的非局部均值圖像去噪方法

孫偉峰,  戴永壽

孫偉峰, 戴永壽. 采用多級殘差濾波的非局部均值圖像去噪方法[J]. 電子與信息學(xué)報, 2016, 38(8): 1999-2006. doi: 10.11999/JEIT151227
引用本文: 孫偉峰, 戴永壽. 采用多級殘差濾波的非局部均值圖像去噪方法[J]. 電子與信息學(xué)報, 2016, 38(8): 1999-2006. doi: 10.11999/JEIT151227
SUN Weifeng, DAI Yongshou. Non-local Means Image Denoising with Multi-stage Residual Filtering[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1999-2006. doi: 10.11999/JEIT151227
Citation: SUN Weifeng, DAI Yongshou. Non-local Means Image Denoising with Multi-stage Residual Filtering[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1999-2006. doi: 10.11999/JEIT151227

采用多級殘差濾波的非局部均值圖像去噪方法

doi: 10.11999/JEIT151227
基金項目: 

國家自然科學(xué)基金(61501520)，山東省自然科學(xué)基金(ZR2013FL035)，中央高?；究蒲袠I(yè)務(wù)費專項資金(14CX02083A)

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

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: 為充分利用殘差中的圖像信息以提升非局部均值算法的去噪性能，該文提出一種多級殘差圖像濾波新方法。首先對含噪圖像進(jìn)行非局部均值濾波得到初始的去噪圖像和權(quán)值分布矩陣，然后對殘差圖像進(jìn)行固定權(quán)值非局部均值濾波來提取圖像結(jié)構(gòu)信息，將提取的信息經(jīng)高斯平滑抑噪后作為補償圖像，與去噪圖像相加得到增強的恢復(fù)圖像。針對上述方法提出一種多級濾波的實現(xiàn)方案，從理論上推導(dǎo)證明了該方法的原理及可行性，并提出一種無需參考圖像的迭代停止準(zhǔn)則來自適應(yīng)地優(yōu)選濾波級數(shù)。實驗結(jié)果表明，提出的迭代停止準(zhǔn)則能夠達(dá)到與峰值信噪比一致的優(yōu)選結(jié)果；與經(jīng)典的非局部均值算法相比，在計算效率相當(dāng)?shù)那闆r下，所提方法能夠顯著地提升其去噪性能，峰值信噪比平均可以提高1.2 dB，且具有更好的細(xì)節(jié)保持能力。
    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.
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出版歷程
  • 收稿日期:  2015-11-03
  • 修回日期:  2016-03-04
  • 刊出日期:  2016-08-19

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