论文标题
基于空间变化的广义高斯模型的联合图像恢复和特征提取的变异方法
A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models
论文作者
论文摘要
重建 /特征提取的联合问题是图像处理中的一项具有挑战性的任务。它包括以联合方式执行图像的恢复及其特征的提取。在这项工作中，我们首先提出了一种新型的非平滑和非凸变性表述。为此，我们介绍了一种通用的高斯先验，其参数（包括其指数）是空间变化的。其次，我们设计了一种基于近端的交替优化算法，该算法有效利用了所提出的非凸目标函数的结构。我们还分析了该算法的收敛性。如在关节脱毛/分割任务上进行的数值实验中所示，所提出的方法提供了高质量的结果。
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.
