论文标题
高效且平行的可分离字典学习
Efficient and Parallel Separable Dictionary Learning
论文作者
论文摘要
词典可分开或kronecker产品为2D信号（例如图像）提供了自然分解。在本文中，我们描述了一种高度可行的算法，该算法学习了此类词典，该词典达到了与先前的艺术词典学习算法竞争的稀疏表示，但计算成本较低。我们强调了所提出的方法的性能，以稀疏表示图像和高光谱数据，以及图像降解。
Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising.
