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Image Denoising Using Fractional Quaternion  Wavelet Transform

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Proceedings of 2nd International Conference on Computer Vision & Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 704))

Abstract

This paper presents an image denoising algorithm using fractional quaternion wavelet transform (FrQWT). In particular, images corrupted with additive Gaussian noise are considered and FrQWT is performed via hard and semi-soft thresholds. The thresholding on the wavelet coefficients reveals the capabilities of wavelet transform in the restoration of an image degraded by noise. FrQWT is simple and adaptive since the estimation of threshold parameters depends on the data of wavelet coefficients. The fractional order captures the texture details of an image in more adaptive way. Experimental results exploit the better performance compared to the various techniques such as denoising in discrete wavelets, complex wavelet and quaternion wavelet transform domains in terms of high peak signal-to-noise ratio (PSNR).

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Acknowledgements

One of the authors, Savita, gratefully acknowledges the financial support of the Ministry of Human Resources and Development, New Delhi, India, during her Ph.D. work.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Savita Nandal & Sanjeev Kumar

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  1. Savita Nandal
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  2. Sanjeev Kumar
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Correspondence to Savita Nandal .

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Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Nandal, S., Kumar, S. (2018). Image Denoising Using Fractional Quaternion  Wavelet Transform. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_25

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  • DOI: https://doi.org/10.1007/978-981-10-7898-9_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7897-2

  • Online ISBN: 978-981-10-7898-9

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