Fast solver for some computational imaging problems: A regularized weighted least-squares approach

被引:9
作者
Zhang, B. [1 ]
Makram-Ebeid, S. [1 ]
Prevost, R. [1 ]
Pizaine, G. [1 ]
机构
[1] Medisys, Philips Res, Suresnes, France
关键词
Regularized weighted least-squares; Preconditioned conjugate gradient; Preconditioning; Condition number; ANISOTROPIC DIFFUSION; MINIMIZATION; ALGORITHM; RECOVERY;
D O I
10.1016/j.dsp.2014.01.007
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper we propose to solve a range of computational imaging problems under a unified perspective of a regularized weighted least-squares (RWLS) framework. These problems include data smoothing and completion, edge-preserving filtering, gradient-vector flow estimation, and image registration. Although originally very different, they are special cases of the RWLS model using different data weightings and regularization penalties. Numerically, we propose a preconditioned conjugate gradient scheme which is particularly efficient in solving RWLS problems. We provide a detailed analysis of the system conditioning justifying our choice of the preconditioner that improves the convergence. This numerical solver, which is simple, scalable and parallelizable, is found to outperform most of the existing schemes for these imaging problems in terms of convergence rate. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:107 / 118
页数:12
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