Non-Convex High-Order TV and ℓ 0 -Norm Wavelet Frame-Based Speckle Noise Reduction

To obtain natural restorations from the noisy images contaminated by speckle noise, this brief presents a novel hybrid non-convex regularizers model for image denoising. The proposed new variational model closely combines the superiorities of non-convex high-order total variation function and [Formu...

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Veröffentlicht in:	IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2022-12, Vol.69 (12), p.5174-5178
Hauptverfasser:	Liu, Xinwu, Lian, Wenhui
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Sprache:	eng
Schlagworte:	Algorithms Mathematical analysis Mathematical models Noise reduction Numerical methods Optimization
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container_title	IEEE transactions on circuits and systems. II, Express briefs
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creator	Liu, Xinwu Lian, Wenhui
description	To obtain natural restorations from the noisy images contaminated by speckle noise, this brief presents a novel hybrid non-convex regularizers model for image denoising. The proposed new variational model closely combines the superiorities of non-convex high-order total variation function and [Formula Omitted]-norm wavelet frame. This combination helps to avoid the staircase artifacts and maintain discontinuities while removing noise. Numerically, by integrating two popular tools: iteratively reweighted [Formula Omitted] algorithm and variable splitting method, a modified alternating minimization method is adopted to optimize the resulting minimization problem. Finally, compared with several despeckling methods, numerical experiments indicate the competitive performance of our solver in visual improvement and objective measurement.
doi_str_mv	10.1109/TCSII.2022.3197237
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title	Non-Convex High-Order TV and ℓ 0 -Norm Wavelet Frame-Based Speckle Noise Reduction
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