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Partition-based weighted sum filters for image restoration.
K E Barner1, A M Sarhan, R C Hardie
1Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA. barner@ee.udel.edu
Summary
We introduce partition-based weighted sum (PWS) filters, a novel approach to signal processing. These filters adapt to data nonstationarities, outperforming existing methods in image restoration tasks.
Area of Science:
- Signal Processing
- Image Restoration
- Machine Learning
Background:
- Traditional filters struggle with nonstationary data, leading to loss of detail.
- Existing methods often lack adaptability to varying data characteristics.
Purpose of the Study:
- To develop a general class of adaptive filters, termed partition-based weighted sum (PWS) filters.
- To enhance detail preservation and improve performance in signal and image processing.
Main Methods:
- Partitioning the observation space using vector quantization.
- Employing linear filtering functions within each partition.
- Training partitions and weights on representative data for adaptive processing.
Main Results:
- PWS filters demonstrate superior performance compared to previously defined filters.
- Achieved greater detail preservation in the presence of data nonstationarities.
- Experimental results show effectiveness in restoring images corrupted by Gaussian noise.
Conclusions:
- PWS filters offer a flexible and effective framework for adaptive signal processing.
- The proposed method provides significant improvements in image restoration quality.
- Data-adaptive processing via partitioning enhances filter performance in challenging conditions.
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