A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models
This work addresses a challenging image processing task for applications like medical imaging or computer vision, but it appears incremental as it builds on existing variational and optimization frameworks.
The authors tackled the joint problem of image reconstruction and feature extraction by proposing a novel variational formulation with a spatially-varying generalized Gaussian prior and an alternating proximal optimization algorithm. Their method achieved high-quality results in joint deblurring/segmentation tasks, as demonstrated in numerical experiments.
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.