Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

TL;DR

This paper introduces a real-time impulse noise removal technique for images that uses an impulse detector and a weighted-average filter, achieving superior quality and efficiency for practical applications.

Contribution

It presents a novel weighted-average filtering approach combined with impulse detection for high-density noise suppression in real-time image processing.

Findings

Outperforms existing methods in PSNR and visual quality

Suitable for real-time applications

Effective at high noise densities

Abstract

In this paper, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.