Cost-Effective Implementation of Order-Statistics Based Vector Filters Using Minimax Approximations

TL;DR

This paper presents minimax approximation techniques to significantly speed up order-statistics based vector filters, maintaining accuracy while reducing computational complexity for real-time color image processing.

Contribution

It introduces minimax approximation methods to enhance the efficiency of vector filters based on order statistics, enabling faster processing in time-critical applications.

Findings

Achieved high accuracy with simplified filter computations.

Significantly reduced processing time for color image filtering.

Maintained edge preservation and noise reduction quality.

Abstract

Vector operators based on robust order statistics have proved successful in digital multichannel imaging applications, particularly color image filtering and enhancement, in dealing with impulsive noise while preserving edges and fine image details. These operators often have very high computational requirements which limits their use in time-critical applications. This paper introduces techniques to speed up vector filters using the minimax approximation theory. Extensive experiments on a large and diverse set of color images show that proposed approximations achieve an excellent balance among ease of implementation, accuracy, and computational speed.