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A new multiplicative denoising variational model based on mth root transformation.
1Department of Mathematics Education, Sung Kyun Kwan University, Seoul, Korea. sangwoony@gmail.com
Summary
This study introduces an m-th root function to improve multiplicative noise removal in synthetic aperture radar (SAR) images. The new method offers better performance and efficiency than existing log-transformed models.
Area of Science:
- Image processing
- Signal processing
- Remote sensing
Background:
- Coherent imaging systems like synthetic aperture radar (SAR) suffer from multiplicative noise.
- Variational models with total variation (TV) regularization are effective for noise reduction but often face non-convexity issues.
- Existing methods use logarithmic transformation to achieve convexity, which can be computationally challenging.
Purpose of the Study:
- To develop a novel variational model for multiplicative noise removal in SAR images.
- To address the non-convexity problem in fidelity terms without using logarithmic transformation.
- To propose an efficient and parallelizable algorithm for noise reduction in large-scale SAR data.
Main Methods:
- Exploited an m-th root function to relax the non-convexity of the variational model.
- Adapted a linearized proximal alternating minimization algorithm to solve the proposed model efficiently.
- Avoided inner iterations for subproblem solving, enhancing computational efficiency.
Main Results:
- The proposed m-th root transformed model demonstrates superior performance in multiplicative noise removal compared to the log-transformed convex model.
- The adapted algorithm is simple, highly parallelizable, and efficient for processing large SAR images.
- The method effectively preserves image edges while reducing multiplicative noise.
Conclusions:
- The m-th root function offers a viable alternative to logarithmic transformation for addressing non-convexity in variational models for SAR image noise reduction.
- The linearized proximal alternating minimization algorithm provides an efficient and practical solution for the proposed model.
- This approach significantly improves the quality of SAR images affected by multiplicative noise.
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