Coupled K-SVD dictionary training for super-resolution

Xu, J. Q.; Qi, Chun; Chang, Zhiguo

doi:10.1109/icip.2014.7025794

Cited by 21 publications

(28 citation statements)

References 22 publications

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“…Because the SISR problem depends on the invariance of the sparse coefficients. The idea of single dictionary learning with no coupling between the sparse coefficients has already been superseded by [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…In [12], the authors proposed a coupled dictionary learning mechanism for training of HR and LR dictionaries. In this setup, an alternate mechanism is applied to the sparse coefficients of HR and LR patches; for each iteration, one sparse coefficient is chosen either the HR or LR and it is used to update both the HR and LR dictionaries.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of using a single pair of dictionaries as done in [11] and [12], multiple directional dictionaries are proposed as done in [10]. The training data is divided into eight directional clusters and a non-directional one.…”

Section: Introductionmentioning

confidence: 99%

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Single image super-resolution by directionally structured coupled dictionary learning

Ahmed

Shah

2016

J Image Video Proc.

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In this paper, a new algorithm is proposed based on coupled dictionary learning with mapping function for the problem of single-image super-resolution. Dictionaries are designed for a set of clustered data. Data is classified into directional clusters by correlation criterion. The training data is structured into nine clusters based on correlation between the data patches and already developed directional templates. The invariance of the sparse representations is assumed for the task of super-resolution. For each cluster, a pair of high-resolution and low-resolution dictionaries are designed along with their mapping functions. This coupled dictionary learning with a mapping function helps in strengthening the invariance of sparse representation coefficients for different resolution levels. During the reconstruction phase, for a given low-resolution patch a set of directional clustered dictionaries are used, and the cluster is selected which gives the least sparse representation error. Then, a pair of dictionaries with mapping functions of that cluster are used for the high-resolution patch approximation. The proposed algorithm is compared with earlier work including the currently top-ranked super-resolution algorithm. By the proposed mechanism, the recovery of directional fine features becomes prominent.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Single image super-resolution by directionally structured coupled dictionary learning

Ahmed

Shah

2016

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Super-resolution image reconstruction algorithms can be basically classified into three categories: the interpolation-based SR methods [1], the reconstruction-based SR methods [2][3] and example-based SR methods [4][5][6][7][8][9][10][11][12][13]. The interpolation-based SR methods are very simple and suitable for real-time applications, but they are of low reconstruction precision and poor restoration details.…”

Section: Introductionmentioning

confidence: 99%

“…Example-based SR approaches break the limitations existing in the traditional reconstruction-based algorithms. They learn the mapping relationship between the corresponding pre-processed low and high resolution training samples to recover the missed HF details, mainly including learningbased approach [4], neighborhood embedding approach [5] and sparse representation methods [6][7][8][9][10][11][12][13][14][15][16][17][18], etc.…”

Section: Introductionmentioning

confidence: 99%