Image Filtering using All Neighbor Directional Weighted Pixels: Optimization using Particle Swarm Optimization

TL;DR

This paper introduces a novel image denoising method that combines neighbor directional weighted pixel detection with particle swarm optimization to enhance noise removal while preserving image details.

Contribution

It proposes a new two-step denoising approach using all neighbor directional weighted pixels and optimizes parameters with particle swarm optimization for improved performance.

Findings

Better noise suppression in highly corrupted images

Preservation of fine image details

Effective parameter optimization using PSO

Abstract

In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) has also been used for searching the parameters of detection and filtering operators required for optimal performance. Results obtained shows better de noising and preservation of fine details for highly corrupted images.