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Poisson Noise-Adapted Total Variation-Based Filter for Restoration and Enhancement of Mammogram Images

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Advance Concepts of Image Processing and Pattern Recognition

Part of the book series: Transactions on Computer Systems and Networks ((TCSN))

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Abstract

In this manuscript, we introduce a total variation to eradicate noise from an image which the Poisson noise has corrupted. This method regulates the total variation in order to preserve edges. Apart from that, it employs the fidelity of the data term that is better suited to the Poisson noise. A diversity of approaches for denoising mammogram pictures has been described, each with its own assumptions, advantages, and limitations. The efficacy of the filters has also been compared using criteria such as MSE and PSNR. Like the Poisson noise, the result of this regularization is also signaling dependent. The design steps include preprocessing. The preprocessing techniques include manual cropping of original mammograms to remove background details, quantum noise reduction, and contrast enhancements. A modified TV-based filter with the Poisson noise adaptation is utilized to reduce quantum noise.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication, G.B. Pant Institute of Engineering and Technology, Pauri Garhwal, India

    Sneha Tiruwa

  2. Department of Electronics and Communication, G. B. Pant Institute of Engineering and Technology (Formerly G. B. Pant Engineering College), Pauri Garhwal, India

    R. B. Yadav

  3. Department of Electronics and Communication Engineering, Delhi Technological University (Formerly Delhi College of Engineering), New Delhi, India

    Nikhil Singh

Authors
  1. Sneha Tiruwa
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  2. R. B. Yadav
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  3. Nikhil Singh
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Editor information

Editors and Affiliations

  1. School of Computing, DIT University, Dehradun, India

    Narendra Kumar

  2. Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Celia Shahnaz

  3. Research and Development Unit, UJVN Ltd., Dehradun, India

    Krishna Kumar

  4. College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

    Mazin Abed Mohammed

  5. Department of Computer Science and Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India

    Ram Shringar Raw

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Tiruwa, S., Yadav, R.B., Singh, N. (2022). Poisson Noise-Adapted Total Variation-Based Filter for Restoration and Enhancement of Mammogram Images. In: Kumar, N., Shahnaz, C., Kumar, K., Abed Mohammed, M., Raw, R.S. (eds) Advance Concepts of Image Processing and Pattern Recognition. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-9324-3_10

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  • DOI: https://doi.org/10.1007/978-981-16-9324-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9323-6

  • Online ISBN: 978-981-16-9324-3

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