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
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.
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页码:74 / 85
页数:12
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