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In this paper we present an extension to the algorithm of super-resolution via selective sparse representation over a set of coupled low and high resolution cluster dictionary pairs. Patch clustering and sparse model selection are carried out using the magnitude and phase of the patch gradient operator. A compact dictionary pair is learned for each cluster. A low resolution patch is classified into...
The dictionary learning process aims at training for a dictionary that can loyally and sparsely represent data in a given training set. In this paper, we propose performing a second pass of dictionary learning where the training set is composed of the residuals of the original training set as calculated with respect to the outcome of a first pass of dictionary learning. In the second pass, the dictionary...
A new strategy for multiple structured dictionary learning is proposed. It is motivated by the fact that a signal and its residual after sparse approximation do not necessarily possess the same geometric structure. Based on the geometric structure of each residual component, the most appropriate dictionary is selected. A single-atom sparse representation vector of this residual is calculated and the...
This paper proposes an extension to the algorithm of single-image super-resolution based on selective sparse representation over a set of coupled low and high resolution dictionary pairs. The extended algorithm reserves the sparse representation framework for patches of high sharpness values while bicubic interpolation is used to super-resolve un-sharp patches. A set of cluster dictionary pairs is...
In this paper a new algorithm for single-image super-resolution based on sparse representation over a set of coupled low and high resolution dictionary pairs is proposed. The sharpness measure is defined via the magnitude of the gradient operator and is shown to be approximately scale-invariant for low and high resolution patch pairs. It is employed for clustering low and high resolution patches in...
We propose a single-image super-resolution algorithm based on sparse representation over a set of cluster dictionary pairs. For each cluster, a directionally structured dictionary pair is designed. The dominant angle in the patch gradient phase matrix is employed as an approximately scale-invariant measure. This measure serves for patch clustering and sparse model selection. The dominant phase angle...
In this paper a new dictionary learning algorithm is proposed. Similar to many dictionary learning algorithms, the proposed algorithm alternates between two stages. First, sparse coding stage uses the current dictionary to obtain the sparse representation coefficients. Herein, the orthogonal matching pursuit algorithm is used for sparse coding. Second, a dictionary update stage that employs the calculated...
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