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Translation-invariant contourlet transform and its application to image denoising
1Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada. reslami@ieee.org
Summary
This study introduces translation-invariant (TI) contourlet transforms for improved image denoising. The novel methods enhance filter banks, offering a robust solution for noise reduction applications.
Area of Science:
- Signal Processing
- Image Analysis
- Computer Vision
Background:
- Subsampled filter banks often lack translation invariance, a crucial property for effective image denoising.
- Existing methods struggle to incorporate translation invariance into multidimensional filter bank designs.
Purpose of the Study:
- To develop novel methods for converting general filter banks into translation-invariant (TI) frameworks.
- To introduce efficient and practical TI transform schemes for image denoising applications.
Main Methods:
- Proposed a generalized algorithme à trous, extending the 1-D wavelet transform concept.
- Constructed the translation-invariant contourlet transform (TICT) using the generalized algorithme à trous and directional filter banks.
- Introduced the semi-translation-invariant contourlet transform (STICT) to mitigate TICT's complexity and redundancy.
Main Results:
- The adapted bivariate shrinkage scheme applied to STICT demonstrated efficient image denoising capabilities.
- Experimental results validated the effectiveness and potential of the proposed denoising approach.
- Complexity analysis and efficient realization strategies for the TI schemes were successfully presented.
Conclusions:
- The developed TI contourlet transform schemes offer significant improvements for image denoising.
- The STICT, combined with bivariate shrinkage, provides an efficient and practical solution for noise reduction.
- This work advances the field of signal processing by enabling translation-invariant filter bank designs.
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