Light-Field Image Super-Resolution Using Convolutional Neural Network

2017 ◽  
Vol 24 (6) ◽  
pp. 848-852 ◽  
Author(s):  
Youngjin Yoon ◽  
Hae-Gon Jeon ◽  
Donggeun Yoo ◽  
Joon-Young Lee ◽  
In So Kweon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Light Field ◽  
Super Resolution ◽  
Light Field Image ◽  
Image Super Resolution

LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution

2018 ◽  
Vol 27 (9) ◽  
pp. 4274-4286 ◽  
Author(s):  
Yunlong Wang ◽  
Fei Liu ◽  
Kunbo Zhang ◽  
Guangqi Hou ◽  
Zhenan Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Light Field ◽  
Super Resolution ◽  
Light Field Image ◽  
Image Super Resolution

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valli Bhasha A. ◽  
Venkatramana Reddy B.D.
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Discrete Wavelet ◽  
Content Type ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Better Than

Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

Image Super-resolution Based on Tiny Recurrent Convolutional Neural Network

2018 ◽  
Vol 47 (4) ◽  
pp. 410004
Author(s):  
马昊宇 MA Hao-yu ◽  
徐之海 XU Zhi-hai ◽  
冯华君 FENG Hua-jun ◽  
李奇 LI Qi ◽  
陈跃庭 CHEN Yue-ting
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Image Super Resolution
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