期刊
SIAM JOURNAL ON IMAGING SCIENCES
卷 12, 期 2, 页码 1099-1134出版社
SIAM PUBLICATIONS
DOI: 10.1137/18M1199149
关键词
variational method; convex nonconvex strategy; sparsity-promoting regularization
类别
资金
- National Group for Scientific Computation (GNCS-INDAM)
- National Science Foundation [1525398]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1525398] Funding Source: National Science Foundation
A popular strategy for determining solutions to linear least-squares problems relies on using sparsity-promoting regularizers and is widely exploited in image processing applications such as, e.g., image denoising, deblurring, and inpainting. It is well known that, in general, nonconvex regularizers hold the potential for promoting sparsity more effectively than convex regularizers such as, e.g., those involving the l(1) norm. To avoid the intrinsic difficulties related to non-convex optimization, the convex nonconvex (CNC) strategy has been proposed, which allows the use of nonconvex regularization while maintaining convexity of the total objective function. In this paper, a new CNC variational model is proposed, based on a more general parametric nonconvex nonseparable regularizer. The proposed model is applicable to a greater variety of image processing problems than prior CNC methods. We derive the convexity conditions and related theoretical properties of the presented CNC model, and we analyze existence and uniqueness of its solutions. A primal-dual forward-backward splitting algorithm is proposed for solving the related saddle-point problem. The convergence of the algorithm is demonstrated theoretically and validated empirically. Several numerical experiments are presented which prove the effectiveness of the proposed approach.
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