A spatially adaptive hybrid total variation model for image restoration under Gaussian plus impulse noise

Rong Li and Bing Zheng

Applied Mathematics and Computation, 2022, vol. 419, issue C

Abstract: In this paper, a spatially adaptive hybrid total variation model is proposed to recover blurred images corrupted by mixed Gaussian-impulse noise. The model consists of a combined L1/L2 data fidelity term and two regularization terms including total variation and high-order total variation. The spatially adaptive parameters with multiple windows are utilized by the model to adequately smooth homogeneous areas while preserving small features. A strategy for adaptively selecting the locally varying parameters together with a solver of the constituted optimisation problem are presented. Experimental results demonstrate the excellent performance of the new approach compared with current state-of-the-art methods with respect to digital indicators and visual quality.

Keywords: Mixed Gaussian-impulse noise; Combined L1/L2 data fidelity term; Hybrid total variation; Total variation and high-order total variation; Spatially adaptive parameters (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:419:y:2022:i:c:s0096300321009450

DOI: 10.1016/j.amc.2021.126862

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