scholarly journals Super-Resolution of Face Images Using Weighted Elastic Net Constrained Sparse Representation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 55180-55190 ◽  
Author(s):  
Xiaobing Pei ◽  
Tao Dong ◽  
Yue Guan
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Elastic Net ◽  
Face Images

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

2020 ◽  
Vol 10 (2) ◽  
pp. 718 ◽  
Author(s):  
K. Lakshminarayanan ◽  
R. Santhana Krishnan ◽  
E. Golden Julie ◽  
Y. Harold Robinson ◽  
Raghvendra Kumar ◽  
...  
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Expectation Maximization ◽  
Super Resolution ◽  
Integrated Approach ◽  
Face Hallucination ◽  
Resolution Image ◽  
High Resolution Image ◽  
Face Images ◽  
Resolution Algorithm

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.

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

Image super‐resolution via adaptive sparse representation and self‐learning

2018 ◽  
Vol 12 (5) ◽  
pp. 753-761 ◽  
Author(s):  
Jianwei Zhao ◽  
Tiantian Sun ◽  
Feilong Cao
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Self Learning
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