Restoring highly corrupted images by impulse noise using radial basis functions interpolation

TL;DR

This paper introduces a radial basis functions interpolation algorithm for restoring images heavily corrupted by impulse noise, effectively removing noise while preserving details, outperforming recent methods in PSNR and SSIM.

Contribution

The paper presents a novel RBF-based interpolation method specifically designed for restoring images with high-density impulse noise, improving noise suppression and detail preservation.

Findings

Outperforms recent methods in PSNR and SSIM

Effectively removes highly dense impulse noise

Preserves image details during restoration

Abstract

Preserving details in restoring images highly corrupted by impulse noise remains a challenging problem. We proposed an algorithm based on radial basis functions (RBF) interpolation which estimates the intensities of corrupted pixels by their neighbors. In this algorithm, first intensity values of noisy pixels in the corrupted image are estimated using RBFs. Next, the image is smoothed. The proposed algorithm can effectively remove the highly dense impulse noise. Experimental results show the superiority of the proposed algorithm in comparison to the recent similar methods both in noise suppression and detail preservation. Extensive simulations show better results in measure of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), especially when the image is corrupted by very highly dense impulse noise.