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Steerable weighted median filters.

Dimitrios Charalampidis¹

¹Electrical Engineering Department, University of New Orleans, New Orleans, LA 70148, USA. dcharala@uno.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 25, 2009

Summary

This summary is machine-generated.

This study extends filter steerability to weighted median filters (WMFs), a class of nonlinear filters. The research introduces a novel design technique for steerable WMFs, enabling enhanced image processing capabilities like edge detection.

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Area of Science:

Image Processing
Computer Vision
Signal Processing

Background:

Steerable filters are crucial for image processing, but this property is limited to linear filters.
Nonlinear filters like weighted median filters (WMFs) offer advantages such as robustness and edge preservation.
Extending steerability to nonlinear filters could enhance their applicability.

Purpose of the Study:

To extend the concept of filter steerability to weighted median filters (WMFs).
To develop a design technique for steerable WMFs that can handle negative weights.
To demonstrate the practical utility of steerable WMFs in image analysis tasks.

Main Methods:

The study defines steerability for filters based on linear combinations of basis functions for transformed impulse responses.
A novel design approach for steerable WMFs is proposed, specifically addressing the challenge of negative weights.
The performance of the developed steerable WMFs is evaluated through experimental applications.

Main Results:

The research successfully extends steerability to WMFs, a significant advancement for nonlinear filter design.
The proposed WMF design technique effectively handles negative weights, a requirement for steerable designs.
Experimental results validate the applicability of steerable WMFs in edge detection and orientation analysis.

Conclusions:

Steerable weighted median filters (WMFs) offer a powerful new tool for image processing, combining nonlinear advantages with steerability.
The developed design methodology enables the creation of steerable WMFs, overcoming previous limitations.
Steerable WMFs show promise for advanced image analysis tasks, including edge detection and orientation analysis.