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Speckle removal using a maximum-likelihood technique with isoline gray-level regularization.
Nicolas Bertaux1, Yann Frauel, Philippe Réfrégier
1Institut Fresnel, Unité Mixte de Recherche 6133, Domaine Universitaire de Saint Jérôme, 13397 Marseille, France. nicolas.bertaux@fresnel.fr
Summary
This study introduces a maximum-likelihood method to remove speckle noise from coherent images. The technique effectively enhances image quality by modeling continuous gray level variations and using linear interpolation.
Area of Science:
- Image processing
- Coherent imaging
- Signal processing
Background:
- Speckle patterns degrade the quality of coherent images.
- Existing methods may not adequately address continuous gray level variations.
Purpose of the Study:
- To develop a novel method for speckle noise removal in coherent images.
- To address limitations of current techniques for images with continuous gray level variations.
Main Methods:
- A maximum-likelihood technique is employed for speckle removal.
- The image model utilizes a lattice of nodes with linear interpolation for pixel gray levels.
- A constraint on isoline gray levels is introduced for regularization.
Main Results:
- The proposed method effectively removes speckle patterns.
- The technique is suitable for images with continuously varying gray levels.
- Regularization improves the robustness of the solution.
Conclusions:
- The maximum-likelihood approach offers a viable solution for speckle noise reduction.
- The method enhances the quality of coherent images with continuous gray level variations.
- The regularization constraint contributes to a more stable and accurate outcome.