A Statistical Multiresolution Strategy for Image Reconstruction

被引：0
|
作者：
Frick, Klaus [1 ]
Marnitz, Philipp [1 ]
机构：
[1] Univ Gottingen, Inst Math Stochast, D-37077 Gottingen, Germany
来源：
SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION | 2012年 / 6667卷
关键词：
statistical multiresolution; extreme-value statistics; total-variation regularization; statistical inverse problems; statistical imaging; TOTAL VARIATION MINIMIZATION; SADDLE-POINT PROBLEMS; REGULARIZATION; RESTORATION; ALGORITHMS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we present a fully data-driven and locally-adaptive method for image reconstruction that is based on the concept of statistical multiresolution estimation as introduced in [1]. It constitutes a statistical regularization technique that uses a l(infinity)-type distance measure as data fidelity combined with a convex cost functional. The resulting convex optimization problem is approached by a combination of an inexact augmented Lagrangian method and Dykstra's projection algorithm.
引用
收藏
页码：74 / 85
页数：12
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