A Nonlocal Structure Tensor-Based Approach for Multicomponent Image Recovery Problems

作者：Chierchia Giovanni^*; Pustelnik Nelly; Pesquet Popescu Beatrice; Pesquet Jean Christophe

来源：IEEE Transactions on Image Processing, 2014, 23(12): 5531-5544.

DOI：10.1109/TIP.2014.2364141

• Convex optimization
• image restoration
• nonlocal total variation
• structure tensor
• singular value decomposition
• hyperspectral imagery
• epigraph
• multicomponent images

Nonlocal total variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the structure tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the nonlocal variations, jointly for the different components, through various l(1,p)-matrix-norms with p >= 1. To facilitate the choice of the hyperparameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented because of the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for color, multispectral, and hyperspectral images. The results demonstrate the interest of introducing a nonlocal ST regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods, such as the alternating direction method of multipliers.

• 出版日期2014-12
• 单位TELECOM ParisTech