Single-image super-resolution based on Markov random field and contourlet transform

2011 ◽  
Vol 20 (2) ◽  
pp. 023005 ◽  
Author(s):  
Zheng Liu
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Super Resolution ◽  
Contourlet Transform ◽  
Single Image ◽  
Markov Random ◽  
Image Super Resolution ◽  
Single Image Super Resolution

High‐order Markov random field for single depth image super‐resolution

2017 ◽  
Vol 11 (8) ◽  
pp. 683-690 ◽  
Author(s):  
Elham Shabaninia ◽  
Ahmad Reza Naghsh‐Nilchi ◽  
Shohreh Kasaei
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Super Resolution ◽  
High Order ◽  
Depth Image ◽  
Markov Random ◽  
Image Super Resolution

scholarly journals Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

2021 ◽  
Vol 18 (11) ◽  
pp. 5890
Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Model Complexity ◽  
Practical Application ◽  
Single Image ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

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