Sparse representation based image super-resolution on the KNN based dictionaries

Liu, Ning; Xu, Xing; Li, Yujie; Zhu, Anna

doi:10.1016/j.optlastec.2018.01.043

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…The result of comparing the fuzzy version with the Crisp version shows that the fuzzy algorithm has a lower error rate. The kNN algorithm is simple method for solving the classification problem that has competitive results and has higher performance than other data mining techniques [22]. The classifier is tested and proved to be have the capacity to solve the problem of the other algorithms.…”

Section: Classifiersmentioning

confidence: 99%

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

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The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.

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Section: Classifiersmentioning

confidence: 99%

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

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“…eir crucial idea is the discovery of the mapping relationship between LR and HR exemplar image patch pairs. According to the establishment of a mapping relationship, learning-based methods generally include neighbor-based approaches [14,15,18,19], sparse representation-based approaches [8,[20][21][22][23][24][25], example regression-based approaches [9,10,12,13], and deep learning-based approaches [11,16,17,[26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Locally Regularized Collaborative Representation and an Adaptive Low-Rank Constraint for Single Image Superresolution

Gao

Cheng

Chen

et al. 2022

Mobile Information Systems

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Learning-based superresolution reconstruction is an efficient image processing technique that has become a popular topic in recent years. Since superresolution is an ill-conditioned problem, appropriate image priors or examples are the key factors for recovering high-quality images with rich details. However, in some current advanced superresolution methods, the mappings established using dictionaries of low- and high-resolution examples cannot effectively reflect the relationship between the low- and high-resolution spaces. Therefore, we introduce a local structure prior to the collaborative representation to constrain the projection matrix; this structure can better represent the nonlinear mapping between the low- and the high-resolution feature spaces. Then, based on the redundancy of similar image patches, a shape-adaptive low-rank constraint is utilized to explore the images’ nonlocal self-similarity, and the local and nonlocal priors complement each other to enhance the recovered image quality. Finally, an iterative optimization algorithm is adopted to solve our proposed superresolution model. Numerous experiments were performed to verify the proposed method, and the results demonstrate its superiority to some state-of-the-art methods both quantitatively and qualitatively.

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Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening

Nasser

Salim

Hamza

et al. 2018

Advances in Intelligent Systems and Computing

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Sparse representation based image super-resolution on the KNN based dictionaries

Cited by 11 publications

References 36 publications

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Locally Regularized Collaborative Representation and an Adaptive Low-Rank Constraint for Single Image Superresolution

Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening

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