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Fuzzy rank LUM filters
1Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA. ynie@ee.udel.edu
Summary
Fuzzy rank lower-upper-middle (F-LUM) filters enhance image processing by incorporating sample diversity. These novel filters improve noise removal and detail preservation compared to traditional methods.
Area of Science:
- Nonlinear signal processing
- Fuzzy transformation theory
- Image processing
Background:
- Rank information is crucial for nonlinear signal processing algorithms.
- Conventional rank-order filters can be improved by incorporating sample diversity.
- Fuzzy transformation theory introduces fuzzy ranks, combining rank and spread information.
Purpose of the Study:
- Generalize lower-upper-middle (LUM) filters using fuzzy ranks, creating fuzzy rank LUM (F-LUM) filters.
- Analyze the statistical and deterministic properties of F-LUM filters.
- Evaluate the performance of F-LUM filters in image processing tasks.
Main Methods:
- Generalization of LUM filters using fuzzy ranks.
- Derivation of statistical and deterministic properties for F-LUM filters.
- Experimental evaluation for image noise removal, sharpening, and edge detection preprocessing.
Main Results:
- F-LUM smoothers offer comparable noise removal to LUM smoothers but with superior detail preservation.
- F-LUM sharpeners enhance strong edges while preserving fine image details.
- F-LUM filters demonstrate advantages in noise removal, detail preservation, and edge enhancement.
Conclusions:
- F-LUM filters provide a better tradeoff between noise removal and detail preservation than LUM filters.
- F-LUM sharpeners effectively enhance edges without amplifying noise or distorting details.
- The F-LUM filter class offers simplicity, versatility, and improved performance, making them suitable for practical applications.
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