智能技术学报

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稿件标题: A multi-objective optimization framework for ill-posed inverse problems
稿件作者: Maoguo Gong, Hao Li, Xiangming Jiang
关键字词: Ill-posed problem; Image processing; Multi-objective optimization; Evolutionary algorithm
文章摘要: Many image inverse problems are ill-posed for no unique solutions. Most of them have incommensurable or mixed-type objectives. In this study, a multi-objective optimization framework is introduced to model such ill-posed inverse problems. The conflicting objectives are designed according to the properties of ill-posedness and certain techniques. Multi-objective evolutionary algorithms have capability to optimize multiple objectives simultaneously and obtain a set of trade-off solutions. For that reason, we use multi-objective evolutionary algorithms to keep the trade-off between these objectives for image ill-posed problems. Two case studies of sparse reconstruction and change detection are implemented. In the case study of sparse reconstruction, the measurement error term and the sparsity term are optimized by multi-objective evolutionary algorithms, which aims at balancing the trade-off between enforcing sparsity and reducing measurement error. In the case study of image change detec
收录刊物: 2016年1卷3期
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