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A Bayesian Approach to Gaussian-Impulse Noise Removal Using Hessian Norm Regularization

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1776))

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Abstract

The problem of removing mixed noise from images is a challenging problem due to their ill-posed nature. In this paper, we propose a Bayesian technique for the removal of mixed Gaussian-Impulse noise from images. The proposed optimization problem is derived from the maximum a posteriori (MAP) estimates of the noise statistics and makes use of a total variation (TV) and a nuclear norm of the Hessian as its two regularization terms. While TV ensures smoothness to the solution, the use of Hessian takes into account detail preservation in the final optimized output. The proposed problem is then solved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, 801106, India

    Suman Kumar Maji & Anirban Saha

Authors
  1. Suman Kumar Maji
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  2. Anirban Saha
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Corresponding author

Correspondence to Suman Kumar Maji .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Maji, S.K., Saha, A. (2023). A Bayesian Approach to Gaussian-Impulse Noise Removal Using Hessian Norm Regularization. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_17

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  • DOI: https://doi.org/10.1007/978-3-031-31407-0_17

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