An improved image super-resolution reconstruction algorithm based on centralised sparse representation

2013 ◽  
Vol 278-280 ◽  
pp. 1221-1227 ◽  
Author(s):  
Min Fen Shen ◽  
Long Shan Zhang ◽  
Huai Zheng Fu

This paper proposes a YUV color image super-resolution reconstruction algorithm based on sparse representation. The R, G, B components of color image are highly correlated, three-channel super-resolution independent reconstruction will lead to color distortion, so in this paper the color image is firstly converted to the Y, U, V three channels, and then super-resolution reconstruction. For choosing the regularization parameter, this paper proposes an adaptive regularization parameter method; it has a good inhibitory effect on image noise and adaptive super-resolution reconstruction of color images. The results of experiment show that the proposed algorithm has a better PSNR, compared with bicubic interpolation method and sparse representation. The adaptive super-resolution reconstruction can further improve the quality of the reconstructed image and the method is robust to image noise.


Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy

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.


2018 ◽  
Vol 12 (5) ◽  
pp. 753-761 ◽  
Author(s):  
Jianwei Zhao ◽  
Tiantian Sun ◽  
Feilong Cao

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