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Journal Articles Journal of Mathematical Imaging and Vision Year : 2024

A variational approach for joint image recovery and feature extraction based on spatially varying generalised Gaussian models

Abstract

The joint problem of reconstruction/feature extraction is a challenging task in image processing. It consists in performing, in a joint manner, the restoration of an image and the extraction of its features. In this work, we firstly propose a novel non-smooth and non-convex variational formulation of the problem. For this purpose, we introduce a versatile generalised Gaussian prior whose parameters, including its exponent, are space-variant. Secondly, we design an alternating proximal-based optimisation algorithm that efficiently exploits the structure of the proposed non-convex objective function. We also analyse the convergence of this algorithm. As shown in numerical experiments conducted on joint deblurring/segmentation tasks, the proposed method provides high-quality results.
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Dates and versions

hal-03591742 , version 1 (28-02-2022)
hal-03591742 , version 2 (11-10-2023)
hal-03591742 , version 3 (18-03-2024)

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  • HAL Id : hal-03591742 , version 3

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Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Gabriele Scrivanti. A variational approach for joint image recovery and feature extraction based on spatially varying generalised Gaussian models. Journal of Mathematical Imaging and Vision, inPress. ⟨hal-03591742v3⟩
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