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Authors: Farhan Bashar and Mahmoud R. El-Sakka

Affiliation: The University of Western Ontario, Canada

ISBN: 978-989-758-175-5

Keyword(s): Image Denoising, Additive White Gaussian Noise, BM3D, Adaptive Threshold, Classification, Random Forest Classifier, PSNR, SSIM.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing

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.

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Paper citation in several formats:
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

@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},
}

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

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