A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models

• URL: http://arxiv.org/abs/2209.01375v3
• Date: Tue, 5 Mar 2024 14:50:18 GMT
• Title: A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models
• Authors: Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Gabriele Scrivanti
• Abstract summary: The joint problem of reconstruction / extraction optimisation is a challenging task in image processing. It consists in performing, in a joint manner, the restoration of an image and the extraction of the image.
• Score: 13.952521992627847
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 nonsmooth 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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