# BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

### Farhan Bashar, Mahmoud R. El-Sakka

#### Abstract

Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard threshold value to attenuate noise from a 3D block. Experiment shows that this fixed hard thresholding deteriorates the performance of BM3D because it does not consider the context of corresponding blocks. We propose a learning based adaptive hard thresholding method to solve this problem and found excellent improvement over the original BM3D. Also, BM3D algorithm requires as an input the value of noise level in the input image. But in real life it is not practical to pass as an input the noise level of an image to the algorithm. We also added noise level estimation method in our algorithm without degrading the performance. Experimental results demonstrate that our proposed algorithm outperforms BM3D in both objective and subjective fidelity criteria.

#### References

- Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
- Buades, A., Coll, B., and Morel, J. (2005). A non-local algorithm for image denoising. In Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).
- Chaudhury, K. N. and Singer, A. (2012). Non-local euclidean medians. IEEE Signal Processing Letters, 19(11):745-748.
- Dabov, K., Foi, A., and Egiazarian, K. (2007a). Video denoising by sparse 3d transform-domain collaborative filtering. In Proc. European Signal Processing Conference (EUSIPCO).
- Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2006). Image denoising with block-matching and 3d filtering. In Proc. SPIE Electronic Imaging.
- Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2007b). Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminancechrominance space. In Proc. IEEE International Conference on Image Processing (ICIP).
- Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2007c). Image denoising by sparse 3d transformdomain collaborative filtering. IEEE Transactions on Image Processing, 16(8):2080 - 2095.
- Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2009). Bm3d image denoising with shape-adaptive principal component analysis. In Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS).
- Dai, L., Zhang, Y., and Li, Y. (2013). Bm3d image denoising algorithm with adaptive distance hard-threshold.
- International Journal of Signal Processing, Image Processing and Pattern Recognition, 6(6):41-50.
- Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing. Prentice-Hall Inc.
- Hasan, M. and El-Sakka, M. R. (2015). Structural similarity optimized wiener filter: A way to fight image noise. In Proc. International Conference on Image Analysis and Recognition (ICIAR).
- Lim, S. J. (1990). Two-Dimensional Signal and Image Processing. Prentice Hall, New Jersey, USA.
- Mittal, A., Moorthy, A. K., and Bovik, A. C. (2012). Automatic parameter prediction for image denoising algorithms using perceptual quality features. In Proc. SPIE Human Vision and Electronic Imaging.
- Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629 - 639.
- Rehman, A. and Wang, Z. (2011). Ssim-based non-local means image denoising. In Proc. IEEE International Conference on Image Processing (ICIP).
- Thaipanich, T., Oh, B. T., Wu, P., and Kuo., C. J. (2010). Adaptive nonlocal means algorithm for image denoising. In Proc. IEEE International Conference on Consumer Electronics (ICCE).
- Wang, Z., Bovik, A., Sheikh, H. R., and Simoncelli, E. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612.
- Wiener, N. (1949). The Interpolation, Extrapolation and Smoothing of Stationary Time Series. MIT press, New York.

#### Paper Citation

#### in Harvard Style

Bashar F. and El-Sakka M. (2016). **BM3D Image Denoising using Learning-based Adaptive Hard Thresholding** . In *Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)* ISBN 978-989-758-175-5, pages 204-214. DOI: 10.5220/0005787202040214

#### in Bibtex Style

@conference{visapp16,

author={Farhan Bashar and Mahmoud R. El-Sakka},

title={BM3D Image Denoising using Learning-based Adaptive Hard Thresholding},

booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},

year={2016},

pages={204-214},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005787202040214},

isbn={978-989-758-175-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)

TI - BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

SN - 978-989-758-175-5

AU - Bashar F.

AU - El-Sakka M.

PY - 2016

SP - 204

EP - 214

DO - 10.5220/0005787202040214