Volume 32 Issue 3
May  2023
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XU Xudong, ZHANG Zhihua, M. James C. Crabbe, “Quaternion Quasi-Chebyshev Non-local Means for Color Image Denoising,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 397-414, 2023, doi: 10.23919/cje.2022.00.138
Citation: XU Xudong, ZHANG Zhihua, M. James C. Crabbe, “Quaternion Quasi-Chebyshev Non-local Means for Color Image Denoising,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 397-414, 2023, doi: 10.23919/cje.2022.00.138

Quaternion Quasi-Chebyshev Non-local Means for Color Image Denoising

doi: 10.23919/cje.2022.00.138
Funds:  This work was supported by European Commission Horizon 2020’s Flagship Project (861584) and Taishan Distinguished Professor Fund.
More Information
  • Author Bio:

    Xudong XU was born in 1995. She received the B.S. degree from Beijing University of Technology and Industry, China, and M.S. degree from University of Science and Technology Beijing, China. She is currently a Ph.D. student in the School of Mathematics at Shandong University, China. Her research interests include image processing and remote sensing image classification. (Email: 202120281@sdu.edu.cn)

    Zhihua ZHANG (corresponding author) is a Taishan Distinguished Professor of Big Data in School of Mathematics, Shandong University, Jinan, China. He has published 6 first-authored monographs and over 50 first-authored articles. Prof. Zhang is an Associate Editor of EURASIP Journal on Advances in Signal Processing. (Email: zhangzhihua@sdu.edu.cn)

    M. James C. Crabbe is a Professor and Fellow at Oxford University, Oxford, UK and a Visiting Professor at the University of Reading, UK. He was on the Executive Committee of the UK Deans of Science and was elected a Distinguished Fellow of the Institute of Data Science and Artificial Intelligence. (Email: james.crabbe@wolfson.ox.ac.uk)

  • Received Date: 2022-03-17
  • Accepted Date: 2023-01-07
  • Available Online: 2023-02-23
  • Publish Date: 2023-05-05
  • Quaternion non-local means (QNLM) denoising algorithm makes full use of high degree self-similarities inside images to suppress the noise, so the similarity metric plays a key role in its denoising performance. In this study, two improvements have been made for the QNLM: 1) For low level noise, the use of quaternion quasi-Chebyshev distance is proposed to measure the similarity of image patches and it has been used to replace the Euclidean distance in the QNLM algorithm. Since the quasi-Chebyshev distance measures the maximal distance in all color channels, the similarity of color images measured by quasi-Chebyshev distance can capture the structural similarity uniformly for each color channel; 2) For high level noise, quaternion bilateral filtering has been proposed as the preprocessing step in the QNLM algorithm. Denoising simulations were performed on 110 images of landscape, people, and architecture at different noise levels. Compared with QNLM, quaternion non-local total variation (QNLTV), and non-local means (NLM) variants (NLTV, NLM after wavelet threshold preprocessing, and the color adaptation of NLM), our novel algorithm not only improved PSNR/SSIM (peak signal to noise rate/structural similarity) and figure of merit values by an average of 2.77 dB/8.96% and 0.0491 respectively, but also reduced processing time.
  • https://github.com/sepidsh/Image_denoising_NLM
    https://github.com/Tinrry/BOS_NLTV_v1
    https://github.com/xavirema/nlmc
    https://github.com/helderc/WaveletTransformShrinkThreshold
    https://sourceforge.net/projects/qtfm/
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