Nonlinear Vector Filtering for Impulsive Noise Removal from Color Images

TL;DR

This paper surveys 48 impulsive noise removal filters for color images, categorizes them, compares their performance using multiple criteria, and provides guidance on filter selection.

Contribution

It introduces a unified notation for the filters, compares their effectiveness and efficiency, and proposes a fast approximation method for inverse cosine calculations.

Findings

Performance varies across filters and image domains.

A new approximation improves efficiency without sacrificing accuracy.

Guidelines help select suitable filters based on specific needs.

Abstract

In this paper, a comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally, suggestions are provided on how to choose a filter given certain requirements.