Single image super resolution based on sparse representation via directionally structured dictionaries

Farhadifard, Fahime; Abar, Elham; Nazzal, Mahmoud; Ozkaramanh, Huseyin

doi:10.1109/siu.2014.6830580

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…Furthermore, in [26] it is pointed out that using clustering to design several dictionaries improves quality and reduces computational complexity [27]. In 2014, Farhadifard et al [20] trained eight pairs of directionally structured dictionaries for directional patches and a pair of dictionaries for nondirectional patches. Firstly, the two-dimensional space is divided into eight fixed directions.…”

Section: Direction and Edge Learningmentioning

confidence: 99%

“…In 2012, Zeyde et al [19] improved the efficiency of Yang's method by reducing the dimension of training samples and using K-SVD algorithm to train dictionaries. In 2014, Farhadifard et al [20] presented a single-image SR based on sparse representation via directionally structured dictionaries. It can avoid the problem that using same dictionary for sparse representation of image patches cannot reflect the differences of image patch structure characteristics [21], which exists in Yang et al [18] and Zeyde et al [19].…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the work of [18,20] and considering the importance of learning dictionary, the author presents a novel Direction and Edge dictionaries model for image SR. Firstly a pair of Direction and Edge templates is built to classify the training image patches into two clusters. Then each cluster is studied to get two pairs of HR and LR overcomplete Direction and Edge dictionaries.…”

Section: Introductionmentioning

confidence: 99%

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Image Super‐Resolution Based on Sparse Representation via Direction and Edge Dictionaries

Zhu

Wang

et al. 2017

Mathematical Problems in Engineering

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Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize superresolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.

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Section: Direction and Edge Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Super‐Resolution Based on Sparse Representation via Direction and Edge Dictionaries

Zhu

Wang

et al. 2017

Mathematical Problems in Engineering

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“…Dictionaries were obtained from training the image patches from these clusters. In [10], authors designed nine LR directional dictionaries for solving the single-image SR problem. Here, the LR dictionaries were learned by the K-SVD algorithm [1] and HR dictionaries were obtained by solving a pseudo-inverse problem.…”

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%

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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Cascaded Convolutional Neural Network for Image Super-Resolution

Zhang

Wang

Zheng

et al. 2021

Advances in Artificial Intelligence and Security

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Single image super resolution based on sparse representation via directionally structured dictionaries

Cited by 9 publications

References 7 publications

Image Super‐Resolution Based on Sparse Representation via Direction and Edge Dictionaries

Image Super‐Resolution Based on Sparse Representation via Direction and Edge Dictionaries

Single image super-resolution by directionally structured coupled dictionary learning

Cascaded Convolutional Neural Network for Image Super-Resolution

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