Accurate and efficient method for smoothly space-variant Gaussian blurring.
Timothy Popkin1, Andrea Cavallaro, David Hands
1MMV Group, Queen Mary University of London, E1 4NS, UK. t.popkin@qmul.ac.uk
Summary
This study introduces an efficient algorithm for space-variant Gaussian blurring in images. It uses a specialized filter bank to achieve high accuracy with reduced computational cost, outperforming other methods.
Area of Science:
- Computer Vision
- Image Processing
- Signal Processing
Background:
- Space-variant Gaussian blurring is crucial for image processing tasks like foveated coding.
- Traditional methods for space-variant blurring are computationally expensive.
Purpose of the Study:
- To develop a computationally efficient algorithm for smoothly space-variant Gaussian blurring.
- To approximate perfect space-variant Gaussian blurring with high accuracy and reduced cost.
Main Methods:
- Utilized a specialized filter bank with optimal filters derived from principal component analysis.
- Compared the proposed algorithm against brute force and blended Gaussian pyramid approaches.
Main Results:
- The algorithm approximates perfect space-variant Gaussian blurring to arbitrary accuracy.
- Achieved 10 to 15 dB better approximation than blended Gaussian pyramid blurring with only eight filters.
Conclusions:
- The proposed algorithm offers a significant improvement in computational efficiency for space-variant Gaussian blurring.
- This method is particularly beneficial for applications requiring spatially variant image processing.
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