Denoising multispectral images using non-local rank tensor decomposition and bilateral filtering based on sunflower optimization

ABSTRACT

Image denoising is an important pre-processing process in the fields of computer vision and image processing. Traditional denoising techniques blur edges excessively and degrade image quality by removing noise components but failing to maintain clarity. To overcome these problems, this paper proposes a multispectral image denoising strategy combining non-local rank tensor decomposition (NLRTD) and bilateral filtering. To extract patches from noisy images, single-level discrete wavelet transform (DWT) is utilized. Then, similar patches from the extracted images are grouped using spectral clustering. After that, mixed noise is reduced by separating clean images from each clustered group using NLRTD. An optimized bilateral filter using Sunflower optimization (SFO) is used for denoising by preserving edge details and is reconstructed using its constituent parts. The effectiveness of the proposed denoising method is assessed using performance matrices, such as BER, PSNR, MSE, RMSE, SNR and SSIM were 0.8544%, 53.21%, 2.41%, 2.41%, 25.06% and 0.90%, respectively.

KEYWORDS:

• Denoising
• bilateral filtering
• sunflower optimization
• non-local rank tensor decomposition
• ‌multispectral image
• MSI Denoising‌
• ‌discrete wavelet transform
• spectral clustering

Acknowledgements

The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-author guides to verify the analysis result and manuscript editing.

Disclosure statement

No potential conflict of interest was reported by the authors.

Compliance with ethical standards

This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal’s editorial board decides not to accept it for publication.

Additional information

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Notes on contributors

Madhuvan Dixit

Madhuvan Dixit is pursuing Ph.D. in Computer Science Engineering from Department of Information Technology, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. His research interests include Image Processing, Machine Learning, Generative AI. Email id: [email protected]

Mahesh Pawar

Dr. Mahesh Pawar, Associate Professor, currently associated with Department of Information Technology, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He received his Ph.D. from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal. His area of interest includes Software Engineering, DBMS, Bigdata & Hadoop, Image Processing, Machine Learning, Generative AI, Large Language Model, Natural Language Processing. Email id: [email protected]