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Mixed Noise Removal Using Hybrid Fourth Order Mean Curvature Motion

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Advances in Signal Processing and Intelligent Recognition Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 425))

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Abstract

Image restoration is one of the fundamental problems in digital image processing. Although there exists a wide variety of methods for removing additive Gaussian noise, relatively few works tackle the problem of removing mixed noise type from images. In this work we utilize a new hybrid partial differential equation (PDE) model for mixed noise corrupted images. By using a combination mean curvature motion (MMC) and fourth order diffusion (FOD) PDE we study a hybrid method to deal with mixture of Gaussian and impulse noises. The MMC-FOD hybrid model is implemented using an efficient essentially non-dissipative (ENoD) scheme for the MMC first to eliminate the impulse noise with a no dissipation. The FOD component is implemented using explicit finite differences scheme. Experimental results indicate that our scheme obtains optimal denoising with mixed noise scenarios and also outperforms related schemes in terms of signal to noise ratio improvement and structural similarity.

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Author information

Authors and Affiliations

  1. Computational Imaging and Visualization Analysis (CiVA) Lab, Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA

    V. B. Surya Prasath

  2. Department of Computer Science and Applications, Gandhigram Rural Institute – Deemed University, Gandhigram, Dindigul, 624 302, Tamil Nadu, India

    P. Kalavathi

Authors
  1. V. B. Surya Prasath
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  2. P. Kalavathi
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Corresponding author

Correspondence to V. B. Surya Prasath .

Editor information

Editors and Affiliations

  1. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. Department of Electrical, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Computer Engineering Department, Vladimir State University, Vladimir Region, Russia

    Sergey Mosin

  6. School of Electrical, Nanyang Technological University, Singapore, Singapore

    Maode Ma

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Surya Prasath, V.B., Kalavathi, P. (2016). Mixed Noise Removal Using Hybrid Fourth Order Mean Curvature Motion. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_53

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  • DOI: https://doi.org/10.1007/978-3-319-28658-7_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28656-3

  • Online ISBN: 978-3-319-28658-7

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