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Speckle Removal Using Dictionary Learning and PnP-Based Fast Iterative Shrinkage Threshold Algorithm
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DOI：10.1109/LGRS.2023.3241191.png)
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Abstract
En 中文
Speckle is a multiplicative granular noise that naturally occurs in the images captured by coherent imaging sensors such as synthetic aperture radar (SAR). It visually degrades the underlying image information and has an impact on subsequent image analysis. This problem is addressed here by developing a sparse representation model and applying an alternating minimization scheme for SAR image despeckling. The proposed method directly deals with the multiplicative noise and the data model is formed by utilizing the speckle statistics. The similar patches are clustered together to adaptively learn the dictionary, and the sparse coefficients are updated using plug-and-play based fast iterative shrinkage threshold algorithm (PnP-FISTA). Finally, the clean image is estimated using Newton's method. Experiments on simulated and practical SAR images signify that the proposed method performs better compared to the state-of-the-art methods in terms of performance metrics and visual assessment.
Keywords:
Radar polarimetry
Dictionaries
Speckle
Machine learning
Synthetic aperture radar
Optimization
Sparse matrices
Denoiser
dictionary learning (DL)
fast iterative shrinkage threshold algorithm (FISTA)
image despeckling
plug-and-play (PnP) prior
sparse modeling
synthetic aperture radar (SAR)
Journal
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