A HYBRID SVD-DIRECTIONAL FILTERING APPROACH FOR HIGH-DENSITY SALT-AND-PEPPER NOISE REMOVAL FROM GRAYSCALE IMAGES

One of the fundamental challenges in image processing is denoising grayscale images affected by high-density salt-and-pepper noise, where existing median, adaptive, and wavelet-based filters often fail to preserve fine details and structure at noise levels above 70%. To address this gap, this study presents a novel hybrid denoising approach that combines directional filtering with singular value decomposition (SVD). The proposed method leverages directional filtering to preserve edge and textural features while SVD-based reconstruction reduces residual noise through adaptive low-rank approximation. This combination introduces a robust solution for grayscale image denoising at extreme noise densities. Experimental results demonstrate significant improvements in PSNR and SSIM over conventional techniques, providing new knowledge on the effectiveness of hybrid filtering-decomposition methods for image restoration tasks, particularly in scenarios where edge preservation and structural integrity are critical.