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Adaptive median filters: new algorithms and results
1A/V R&D Center, Samsung Electron., Suwon City.
Summary
Two new adaptive median filters, RAMF and SAMF, effectively remove impulse noise while preserving image sharpness. These filters offer superior performance compared to existing methods for various noise densities and image types.
Area of Science:
- Image Processing
- Digital Signal Processing
- Computer Vision
Background:
- Impulse noise significantly degrades image quality.
- Standard median filters struggle to preserve image sharpness while removing noise effectively.
Purpose of the Study:
- To develop novel adaptive median filters for robust impulse noise removal.
- To enhance image sharpness preservation during noise reduction.
Main Methods:
- Proposed the ranked-order based adaptive median filter (RAMF).
- Developed the impulse size based adaptive median filter (SAMF).
- Evaluated filter performance using simulations on standard images.
Main Results:
- RAMF outperforms the nonlinear mean L(p) filter in impulse removal and sharpness preservation.
- SAMF demonstrates superior performance over Lin's adaptive scheme, especially for high-density impulse noise.
- Both RAMF and SAMF show better results than standard median filters.
Conclusions:
- The proposed RAMF and SAMF algorithms offer significant improvements in impulse noise removal.
- These adaptive filters provide a better balance between noise suppression and detail preservation.
- The new algorithms are effective for a wide range of impulse noise conditions.
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