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Sparse dictionary learning has attracted enormous interest in image processing and data representation in recent years. To improve the performance of dictionary learning, we propose an efficient block-structured incoherent K-SVD algorithm for the sparse representation of signals. Without relying on any prior knowledge of the group structure for the input data, we develop a two-stage agglomerative...
Video representation is an important and challenging task in the computer vision community. In this paper, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named adaptive video dictionary learning (AVDL), to model a video adaptively. The developed framework is able to capture the dynamics of a moving scene by exploring both...
In this paper we consider the dictionary learning problem for sparse representation. We first show that this problem is NP-hard and then propose an efficient dictionary learning scheme to solve several practical formulations of this problem. Unlike many existing algorithms in the literature, such as K-SVD, our proposed dictionary learning scheme is theoretically guaranteed to converge to the set of...
We propose a novel approach to performing change-detection based on sparse representations and dictionary learning. We operate on observations that are finite support signals, which in stationary conditions lie within a union of low dimensional subspaces. We model changes as perturbations of these subspaces and provide an online and sequential monitoring solution to detect them. This approach allows...
In current color image super-resolution methods, superresolution based on sparse representation achieves state-of-the-art performance. However, the exploited sparse representation models deal with the color images as independent channel planes. Consequently, these approaches process the color pixels as scalar quantity, lacking of accuracy in describing inter-relationship among color channels. In this...
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