基于空间变化广义高斯模型的联合图像恢复与特征提取变分方法
A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models
AI总结：
针对图像恢复与特征提取联合问题，提出空间变化广义高斯先验的非凸变分模型及基于近似的交替优化算法，并证明其收敛性和实验有效性。
摘要：
重建与特征提取的联合问题是图像处理中的一项挑战性任务。它旨在以联合方式执行图像恢复及其特征提取。在本工作中，我们首先提出了一种新颖的非光滑、非凸变分表述。为此，我们引入了一种灵活的广义高斯先验，其包括指数在内的参数是空间变化的。其次，我们设计了一种基于近似的交替优化算法，可有效利用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。在联合去模糊/分割任务上进行的数值实验表明，所提出的方法能够提供高质量结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
