近年来,稀疏表示理论在信号处理、图像处理和计算机视觉等领域引起了广泛关注。很多研究人员提出了基于稀疏模型的算法,从不同视角对稀疏表示进行分类,如稀疏约束中使用的不同范数的方法可分为l_0范数最小化、l_p范数(0
In recent years, sparse representation has attracted wide attention in fields of signal processing, image processing and computer vision. Many researchers have proposed different algorithms based on sparse model. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints. The methods can be roughly categorized into five groups: sparse representation with l_0-norm minimization; sparse representation with l_p-norm(0
引文
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