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

SUN Weifeng; DAI Yongshou

doi:10.11999/JEIT151227

Volume 38 Issue 8

Sep. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 1999-2006

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

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

PDF( 10807 KB)

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

doi: 10.11999/JEIT151227

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)

Received Date: 2015-11-03
Rev Recd Date: 2016-03-04
Publish Date: 2016-08-19

Abstract

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

FullText(HTML)

References(22)

References

BUADES A, COLL B, and MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling and Simulation (SIAM Interdisciplanary Journal), 2005, 4(2): 490-530. doi: 10.1137/040616024.

钟莹, 杨学志, 唐益明, 等. 采用结构自适应块匹配的非局部均值去噪算法[J]. 电子与信息学报, 2013, 35(12): 2908-2915. doi: 10.3724/SP.J.1146.2013.00099.

ZHONG Ying, YANG Xuezhi, TANG Yiming, et al. Non-local means denoising derived from structure-adapted block matching[J]. Journal of Electronics Information Technology, 2013, 35(12): 2908-2915. doi: 10.3724/SP.J.1146.2013.00099.

SUTOUR C, DELEDALLE C A, and AUJOL J F. Adaptive regularization of the nl-means: application to image and video denoising[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3506-3521. doi: 10.1109/TIP.2014.2329448.

LU Lu, JIN Weiqi, and WANG Xia. Non-local means image denoising with a soft threshold[J]. IEEE Signal Processing Letters, 2015, 22(7): 833-837. doi: 10.1109/LSP.2014.2371332.

DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095. doi: 10.1109/TIP.2007.901238.

DELEDALLE C A, DENIS L, and TUPIN F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights[J]. IEEE Transactions on Image Processing, 2009, 18(12): 2661-2672. doi: 10.1109/TIP. 2009.2029593.

罗亮, 冯象初, 张选德, 等. 基于非局部双边随机投影低秩逼近图像去噪算法[J]. 电子与信息学报, 2013, 35(1): 99-105. doi: 10.3724/SP.J.1146.2012.00819.

LUO Liang, FENG Xiangchu, ZHANG Xuande, et al. An image denoising method based on non-local two-side random projection and low rank approximation[J]. Journal of Electronics Information Technology, 2013, 35(1): 99-105. doi: 10.3724/SP.J.1146.2012.00819.

TALEBI H and MILANFAR P. Global image denoising[J]. IEEE Transactions on Image Processing, 2014, 23(2): 755-768. doi: 10.1109/TIP.2013.2293425.

LIU Ganchao, ZHONG Hua, and JIAO Licheng. Comparing noisy patches for image denoising: a double noise similarity model[J]. IEEE Transactions on Image Processing, 2015, 24(3): 862-872. doi: 10.1109/TIP.2014.2387390.

FENG Jianzhou, SONG Li, HUO Xiaoming, et al. An optimized pixel-wise weighting approach for patch-based image denoising[J]. IEEE Signal Processing Letters, 2015, 22(1): 115-119. doi: 10.1109/LSP.2014.2350032.

BRUNET D, VRSCAY E R, and WANG Z. The use of residuals in image denoising[C]. Proceedings of the International Conference on Image Analysis and Recognition, Halifax, 2009: 1-12. doi: 10.1007/978-3-642-02611-9_1.

CHEN J, TANG C K, and WANG J. Noise brush: interactive high quality image noise separation[J]. ACM Transactions on Graphics, 2009, 28(5): 146: 1-10. doi: 10.1145/1618452. 1618492.

PYO Y, PARK RH, and CHANG S. Noise reduction in high-iso images using 3-d collaborative filtering and structure extraction from residual blocks[J]. IEEE Transactions on Consumer Electronics, 2011, 57(2): 687-695. doi: 10.1109/ TCE.2011.5955209.

ZHONG H, YANG C, and ZHANG X H. A new weight for nonlocal means denoising using method noise[J]. IEEE Signal Processing Letters, 2012, 19(8): 535-538. doi: 10.1109/ LSP.2012.2205566.

KUMAR B K S. Image denoising based on non-local means filter and its method noise thresholding[J]. Signal, Image and Video Processing, 2012, 7(6): 1211-1227. doi: 10.1007/ s11760-012-0372-7.

ROMANO Y and ELAD M. Improving K-SVD denoising by post-processing its method noise[C]. IEEE International Conference on Image Processing, Melbourne, 2013: 435-439. doi: 10.1109/ ICIP. 2013.6738090.

ROMANO Y and ELAD M. Boosting of image denoising algorithms[J]. SIAM Journal on Imaging Sciences, 2015, 8(2): 1187-1219. doi: 10.1137/140990978.

KONG X F, LI K, YANG Q X, et al. A new image quality metric for image auto-denoising[C]. 14th IEEE International Conference on Computer Vision, Sydney, 2013: 2888-2895. doi: 10.1109/ICCV.2013.359.

WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. doi: 10.1109/TIP.2003.819861.

CHANG S G, YU B, and VETTERLI B. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1546. doi: 10.1109/83.862633.

Relative Articles

Supplements(0)

Cited By

Proportional views