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

Abstract

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.