Signal Dependent Local Noise Removal Using Weiner Filter Decomposition

P. Nagarathna; Afreen Kubra; G. Tirumala Vasu; Deepti Raj; Anitha Suresh; Samreen Fiza

doi:10.2991/978-94-6463-471-6_143

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Signal Dependent Local Noise Removal Using Weiner Filter Decomposition

Authors

P. Nagarathna¹, Afreen Kubra², G. Tirumala Vasu³, Deepti Raj⁴, Anitha Suresh⁴, Samreen Fiza³^{, *}

¹Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, 560064, Karnataka, India

²Department of Artificial Intelligence and Machine Learning, HKBK College of Engineering, Bangalore, 560045, Karnataka, India

³Department of Electronics & Communication Engineering, Presidency University, Bangalore, 560064, Karnataka, India

⁴Department of Electronics & Telecommunication Engineering, Dayananda Sagar College of Engineering, Bangalore, 560078, Karnataka, India

^*Corresponding author. Email: samreenfiza236@gmail.com

Corresponding Author

Samreen Fiza

Available Online 30 July 2024.

DOI: 10.2991/978-94-6463-471-6_143 How to use a DOI?
Keywords: Image denoising; Weiner filter; PURE LET deconvolution; mixed Poisson-Gaussian noise; BRISQUE; NIQE; PIQE
Abstract: Image denoising finds applications in various fields like remote sensing, photography, biological imaging, astronomy etc. If image is corrupted with single source of noise, then a suitable denoising filter can be used. The major challenge associated with image denoising algorithms is denoising of image corrupted with multiple sources of the noise. Excessive smoothing can arise during the reduction of Additive White Gaussian Noise (AWGN) which can lead to a reduction in the level of detail and structural information and if Poisson noise is removed, then the AWGN components will still be retained in resultant image. To address this issue, we propose the Poisson Unbiased Risk Estimate Linear Expansion of Thresholds (PURE LET) approach that denoises mixed AWGN and Poisson noise images using Weiner filter decomposition. The application of a linear transformation to a filtered image allows for an inaccurate computation of the signal dependent local noise variance in the transform domain. Weiner filter inverts the blur of the image and removes extra noise by decomposing. The quantitative and qualitative analysis was conducted to determine the proposed algorithm’s efficacy.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series: Advances in Computer Science Research
Publication Date: 30 July 2024
ISBN: 10.2991/978-94-6463-471-6_143
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-471-6_143 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - P. Nagarathna
AU  - Afreen Kubra
AU  - G. Tirumala Vasu
AU  - Deepti Raj
AU  - Anitha Suresh
AU  - Samreen Fiza
PY  - 2024
DA  - 2024/07/30
TI  - Signal Dependent Local Noise Removal Using Weiner Filter Decomposition
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1470
EP  - 1481
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_143
DO  - 10.2991/978-94-6463-471-6_143
ID  - Nagarathna2024
ER  -

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