Distance Measures for Reduced Ordering Based Vector Filters

TL;DR

This paper reviews and evaluates various alternative distance measures for reduced ordering vector filters, demonstrating that alternatives to Minkowski metrics can be more effective in noise removal while preserving image details.

Contribution

The paper introduces and assesses alternative distance measures for vector filters, showing they can outperform Minkowski metrics in noise filtering tasks.

Findings

Alternative distance measures outperform Minkowski metrics in noise removal.

Some new measures offer better edge preservation.

Evaluation on diverse images confirms effectiveness and efficiency.

Abstract

Reduced ordering based vector filters have proved successful in removing long-tailed noise from color images while preserving edges and fine image details. These filters commonly utilize variants of the Minkowski distance to order the color vectors with the aim of distinguishing between noisy and noise-free vectors. In this paper, we review various alternative distance measures and evaluate their performance on a large and diverse set of images using several effectiveness and efficiency criteria. The results demonstrate that there are in fact strong alternatives to the popular Minkowski metrics.