Diese Dissertation widmet sich nichtkonvexen und nichtglatten Minimierungsproblemen in der auf dünner Besetztheit basierten mathematischen Bildverarbeitung. Die Arbeit ist in drei Teile gegliedert.Sparsity, in a general sense, plays a vital role in modern signal and image processing. This thesis is devoted to nonconvex and nonsmooth minimization approaches to sparsity-based image processing, which splits into three major parts.vorgelegt von Tao WuAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. und inengl. SpracheGraz, Univ., Diss., 2014OeBB(VLID)33300
These notes describe an approach for the restoration of degraded signals using sparsity. This approa...
Two complementary solution strategies to the least-squares migration problem with sparseness- & cont...
Inverse problems are problems where we want to estimate the values of certain parameters of a system...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
In this thesis, we present new techniques based on the notions of sparsity and scale invariance to d...
International audienceIn recent years, a large amount of multi-disciplinary research has been conduc...
This paper introduces an efficient method for solving nonconvex penalized minimization problems. Th...
Sparse signals are commonly expected in remote sensing and Earth observation. Along with the signifi...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Medical imaging problems, such as magnetic resonance imaging, can typically be modeled as inverse pr...
International audienceThis book presents the state of the art in sparse and multiscale image and sig...
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
This paper addresses the problem of sparsity pattern detection for unknown k-sparse n-dimensional si...
The exploitation of sparsity has significantly advanced the field of radar imaging over the last few...
These notes describe an approach for the restoration of degraded signals using sparsity. This approa...
Two complementary solution strategies to the least-squares migration problem with sparseness- & cont...
Inverse problems are problems where we want to estimate the values of certain parameters of a system...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
In this thesis, we present new techniques based on the notions of sparsity and scale invariance to d...
International audienceIn recent years, a large amount of multi-disciplinary research has been conduc...
This paper introduces an efficient method for solving nonconvex penalized minimization problems. Th...
Sparse signals are commonly expected in remote sensing and Earth observation. Along with the signifi...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Medical imaging problems, such as magnetic resonance imaging, can typically be modeled as inverse pr...
International audienceThis book presents the state of the art in sparse and multiscale image and sig...
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
This paper addresses the problem of sparsity pattern detection for unknown k-sparse n-dimensional si...
The exploitation of sparsity has significantly advanced the field of radar imaging over the last few...
These notes describe an approach for the restoration of degraded signals using sparsity. This approa...
Two complementary solution strategies to the least-squares migration problem with sparseness- & cont...
Inverse problems are problems where we want to estimate the values of certain parameters of a system...