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Affine Non-local Means Image Denoising

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Evolution in Computational Intelligence (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 370))

Abstract

In subsequent activities like image identification and medical diagnosis, digital pictures are frequently damaged by noise during capture and transmission. When damaged photos must be utilized, several denoising techniques have been suggested to increase the accuracy of these jobs. However, the majority of these techniques either call for making assumptions on the statistical characteristics of the corrupting noise or are specifically created solely for a certain kind of noise. The performance of traditional image denoising algorithms that use lone noisy images and general image databases will soon be reached. In this article, we suggest denoising images with specific external image databases. Denoising is formulated as an optimum filter design issue, and we use the focused databases to (1) find the fundamental properties of the ideal filter using group sparsity, and (2) find spectral coefficients of the optimal filter using localized priors. Research exhibits better denoising outcomes than current methods for a range of circumstances, adding photos and text, multitier pictures, as well as face images. In contrast to employing a general database, we present an adaptive picture denoising approach in this study. In this context, a focused database is one that only contains photos pertinent to the noisy image. Targeted external databases might be acquired in several real-world settings, including text pictures, human faces, and photos taken by multi-view camera systems, as will be demonstrated in subsequent sections of this work.

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Author information

Authors and Affiliations

  1. Department of ECE, G. B. Pant DSEU Okhla-1 Campus (Formerly G. B. Pant Engineering College), New Delhi, 110020, India

    Rohit Anand

  2. Department Computer Science, GIET Degree College, NH-16, Chaitanya Knowledge City, Rajahmundry, Andhra Pradesh, 533294, India

    Valli Madhavi Koti

  3. Department of CSE and CSA, Arni University, Kangra, Himachal Pradesh, India

    Mamta Sharma

  4. Department of Engineering and Technology, Bharati Vidyapeeth Deemed University, Navi Mumbai, India

    Supriya Sanjay Ajagekar

  5. Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Dharmesh Dhabliya

  6. Department of Computer Science and Engineering, Vaish College of Engineering, Rohtak, Haryana, India

    Ankur Gupta

Authors
  1. Rohit Anand
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  2. Valli Madhavi Koti
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  3. Mamta Sharma
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  4. Supriya Sanjay Ajagekar
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  5. Dharmesh Dhabliya
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  6. Ankur Gupta
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Corresponding author

Correspondence to Rohit Anand .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Middlesex University, London, UK

    Xin-She Yang

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Marta Campos Ferreira

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Carlos M. Travieso-Gonzalez

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Anand, R., Koti, V.M., Sharma, M., Ajagekar, S.S., Dhabliya, D., Gupta, A. (2023). Affine Non-local Means Image Denoising. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_45

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