Noise robust position-patch based face super-resolution via Tikhonov regularized neighbor representation

Jiang, Junjun; Chen, Chen; Huang, Kebin; Cai, Zhihua; Hu, Ruimin

doi:10.1016/j.ins.2016.05.032

Cited by 68 publications

(21 citation statements)

References 36 publications

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“…The implementation requires an i3 processor with a 4GB RAM on MATLAB R2018b using the Windows 7 operating system. The proposed method was compared to related methods, e.g., SRGAN [28], TRNR [27], and LSR [26]. Here, the time taken analysis is denoted by the training phase image value.…”

Section: Performance Evaluationmentioning

confidence: 99%

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A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

et al. 2020

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This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

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Section: Performance Evaluationmentioning

confidence: 99%

“…The critical value of the mapping coefficient in LR to HR is computed in the TRNR [27]. Accuracy is maintained in the training vector using subspace matching, and data with dissimilar scales are computed for representation.…”

mentioning

confidence: 99%

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

et al. 2020

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“…ereby, [38,39] proposed algorithms to deal with outliers and sparse errors; however, they still need to be improved. erefore, the search of an affine transformation and Tikhonov regularization term is required to improve the performance of algorithms.…”

Section: Introductionmentioning

confidence: 99%

New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization

Likassa

Xian

Tang

2020

International Journal of Mathematics and Mathematical Sciences

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In this work, a new robust regularized shrinkage regression method is proposed to recover and align high-dimensional images via affine transformation and Tikhonov regularization. To be more resilient with occlusions and illuminations, outliers, and heavy sparse noises, the new proposed approach incorporates novel ideas affine transformations and Tikhonov regularization into high-dimensional images. The highly corrupted, distorted, or misaligned images can be adjusted through the use of affine transformations and Tikhonov regularization term to ensure a trustful image decomposition. These novel ideas are very essential, especially in pruning out the potential impacts of annoying effects in high-dimensional images. Then, finding optimal variables through a set of affine transformations and Tikhonov regularization term is first casted as mathematical and statistical convex optimization programming techniques. Afterward, a fast alternating direction method for multipliers (ADMM) algorithm is applied, and the new equations are established to update the parameters involved and the affine transformations iteratively in the form of the round-robin manner. Moreover, the convergence of these new updating equations is scrutinized as well, and the proposed method has less time computation as compared to the state-of-the-art works. Conducted simulations have shown that the new robust method surpasses to the baselines for image alignment and recovery relying on some public datasets.

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“…These SR methods aim to solve natural image SR problem. There are a kind of SR methods that only deal with face images, which are called face SR (face hallucination) [29,54]. In this paper, the former is our concern.…”

Section: Introductionmentioning

confidence: 99%

Piecewise linear regression-based single image super-resolution via Hadamard transform

Sun

Yiu

Jin

et al. 2018

Information Sciences

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Image super-resolution (SR) has extensive applications in surveillance systems, satellite imaging, medical imaging, and ultra-high definition display devices. The state-ofthe-art methods for SR still incur considerable running time. In this paper, we propose a novel approach based on Hadamard pattern and tree search structure in order to reduce the running time significantly. In this approach, LR (low-resolution)-HR (high-resolution) training patch pairs are classified into different classes based on the Hadamard patterns generated from the LR training patches. The mapping relationship between the LR space and the HR space for each class is then learned and used for SR. Experimental results show that the proposed method can achieve comparable accuracy as state-of-the-art methods with much faster running speed. The dataset, pretrained models and source code can be accessed at this URL † .

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Noise robust position-patch based face super-resolution via Tikhonov regularized neighbor representation

Cited by 68 publications

References 36 publications

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization

Piecewise linear regression-based single image super-resolution via Hadamard transform

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