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Fast super-resolution using an adaptive Wiener filter with robustness to local motion.
Russell C Hardie1, Kenneth J Barnard
1Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, Ohio 45469-0232, USA. rhardie@udayton.edu
Optics Express
|October 6, 2012
Summary
This study introduces a robust adaptive Wiener filter (AWF) super-resolution (SR) algorithm. It accurately reconstructs high-resolution images by handling global and local motion, improving image quality for various applications.
Area of Science:
- Image Processing
- Computer Vision
- Remote Sensing
Background:
- Super-resolution (SR) algorithms enhance image detail.
- Existing SR methods struggle with local motion.
- Accurate registration is crucial for SR.
Purpose of the Study:
- To develop a robust adaptive Wiener filter (AWF) super-resolution (SR) algorithm.
- To improve SR performance in the presence of local motion.
- To enable accurate image reconstruction from multiple frames.
Main Methods:
- Employed a global background motion model for subpixel registration.
- Integrated local motion detection and exclusion to ensure robustness.
- Utilized a modified multiscale registration with pixel selection.
- Performed nonuniform interpolation and image restoration simultaneously.
Main Results:
- The proposed AWF-SR algorithm effectively handles both global and local motion.
- Demonstrated improved resolution and image quality on diverse datasets.
- Airborne infrared data with moving vehicles showed successful application.
- Objective resolution analysis confirmed the method's efficacy.
Conclusions:
- The new robust SR method significantly enhances image resolution.
- The algorithm is effective for datasets with complex motion patterns.
- This approach offers improved performance for surveillance and remote sensing applications.
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